Internet Draft
Document: <draft-ietf-psamp-sample-tech-10.txt> T. Zseby
Intended status: Proposed Standard Fraunhofer FOKUS
Expires: December 2007 M. Molina
DANTE
N. Duffield
AT&T Labs-Research
S. Niccolini
NEC Europe Ltd.
F. Raspall
EPSC-UPC
June 2007
Sampling and Filtering Techniques for IP Packet Selection
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Abstract
This document describes Sampling and Filtering techniques for IP
packet selection. It provides a categorization of schemes and
defines what parameters are needed to describe the most common
selection schemes. Furthermore it shows how techniques can be
combined to build more elaborate packet Selectors. The document
provides the basis for the definition of information models for
configuring selection techniques in Metering Processes and for
reporting the technique in use to a Collector.
Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described
in RFC 2119 [RFC2119].
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Table of Contents
1. Introduction.................................................4
2. PSAMP Documents Overview.....................................4
3. Terminology..................................................5
3.1 Observation Points, Packet Streams and Packet Content.....5
3.2 Selection Process.........................................6
3.3 Reporting.................................................7
3.4 Metering Process..........................................8
3.5 Exporting Process.........................................8
3.6 PSAMP Device..............................................8
3.7 Collector.................................................9
3.8 Selection Methods.........................................9
4. Categorization of Packet Selection Techniques...............11
5. Sampling....................................................13
5.1 Systematic Sampling......................................14
5.2 Random Sampling..........................................15
5.2.1 n-out-of-N Sampling......................................15
5.2.2 Probabilistic Sampling...................................15
5.2.2.1 Uniform Probabilistic Sampling...........................15
5.2.2.2 Non-Uniform Probabilistic Sampling.......................16
5.2.2.3 Non-Uniform Flow State Dependent Sampling................16
5.2.2.4 Configuration of non-uniform probabilistic and flow-
state Sampling..........................................17
6. Filtering...................................................17
6.1 Property Match Filtering.................................17
6.2 Hash-based Filtering.....................................19
6.2.1 Application Examples for Hash-based Selection............20
6.2.1.1 Approximation of Random Sampling.........................20
6.2.1.2 Trajectory Sampling and Consistent Packet Selection......20
6.2.2 Security Considerations for Hash Functions...............21
6.2.2.1 Vulnerabilities of Hash-based selection without
knowledge of selection outcomes.........................22
6.2.2.2 Vulnerabilities of Hash-based selection using knowledge
of selection outcomes...................................23
6.2.2.3 Vulnerabilities to Replay Attacks........................24
6.2.3 Choice of Hash-Function..................................25
6.2.3.1 Properties of some hash functions........................25
6.2.3.2 Hash Functions for Packet Selection......................26
6.2.3.3 Hash Functions Suitable for Packet Digesting.............27
7. Parameters for the Description of Selection Techniques......27
7.1 Description of Sampling Techniques.......................28
7.2 Description of Filtering Techniques......................29
8. Composite Techniques........................................31
8.1 Cascaded Filtering->Sampling or Sampling->Filtering......31
8.2 Stratified Sampling......................................32
9. Security Considerations.....................................32
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10. Acknowledgements............................................33
11. IANA Considerations.........................................33
12. Normative References........................................33
13. Informative References......................................33
Authors' Addresses...............................................36
Intellectual Property Statement..................................37
Copyright Statement..............................................37
Appendix A: Hash Functions.......................................38
A.1 IP Shift-XOR (IPSX) Hash Function............................38
A.2 BOB Hash Function............................................39
1. Introduction
There are two main drivers for the growth in measurement
infrastructures and their underlying technology. First, network
data rates are increasing, with a concomitant growth in
measurement data. Secondly, the growth is compounded by the
demand by measurement-based applications for increasingly fine
grained traffic measurements. Devices such as routers, which
perform the measurements, require increasingly sophisticated and
resource intensive measurement capabilities, including the
capture of packet headers or even parts of the payload, and
classification for flow analysis. All these factors can lead to
an overwhelming amount of measurement data, resulting in high
demands on resources for measurement, storage, transport and
post processing.
The sustained capture of network traffic at line rate can be
performed by specialized measurement hardware. However, the cost
of the hardware and the measurement infrastructure required to
accommodate the measurements preclude this as a ubiquitous
approach. Instead some form of data reduction at the point of
measurement is necessary. This can be achieved by an intelligent
packet selection through Sampling, Filtering, or aggregation.
The motivation for Sampling is to select a representative subset
of packets that allow accurate estimates of properties of the
unsampled traffic to be formed. The motivation for Filtering is
to remove all packets that are not of interest. Aggregation
combines data and allows compact pre-defined views of the
traffic. Examples of applications that benefit from packet
selection are given in [PSAMP-FW]. Aggregation techniques are
out of scope of this document.
2. PSAMP Documents Overview
[PSAMP-FW]: "A Framework for Packet Selection and Reporting"
describes the PSAMP framework for network elements
to select subsets of packets by statistical and
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other methods, and to export a stream of reports
on the selected packets to a Collector.
[PSAMP-TECH]: "Sampling and Filtering Techniques for IP Packet
Selection" (this document) describes the set of
packet selection techniques supported by PSAMP.
[PSAMP-PROTO]: "Packet Sampling (PSAMP) Protocol Specifications"
specifies the export of packet information from a
PSAMP Exporting Process to a PSAMP Colleting
Process.
[PSAMP-INFO]: "Information Model for Packet Sampling Exports"
defines an information and data model for PSAMP.
3. Terminology
The PSAMP terminology defined here is fully consistent with all
terms listed in [PSAMP-FW] but includes additional terms
required for the description of packet selection methods. An
architecture overview and possible configurations of PSAMP
elements can be found in [PSAMP-FW]. PSAMP terminology also aims
at consistency with terms used in [RFC3917]. The relationship
between PSAMP and IPFIX terms is described in [PSAMP-FW].
3.1 Observation Points, Packet Streams and Packet Content
* Observation Point
An Observation Point is a location in the network where
packets can be observed. Examples include:
(i) a line to which a probe is attached;
(ii) a shared medium, such as an Ethernet-based LAN;
(iii) a single port of a router, or set of interfaces
(physical or logical) of a router;
(iv) an embedded measurement subsystem within an interface.
Note that one Observation Point may be a superset of several
other Observation Points. For example one Observation Point
can be an entire line card. This would be the superset of the
individual Observation Points at the line card's interfaces.
* Observed Packet Stream
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The Observed Packet Stream is the set of all packets observed
at the Observation Point.
* Packet Stream
A packet stream denotes a set of packets that flows past some
specified point within the metering process. An example of a
Packet Stream is the output of the selection process.
Note that packets selected from a stream, e.g. by Sampling, do
not necessarily possess a property by which they can be
distinguished from packets that have not been selected. For
this reason the term "stream" is favored over "flow", which is
defined as set of packets with common properties [RFC3917].
* Packet Content
The packet content denotes the union of the packet header
(which includes link layer, network layer and other
encapsulation headers) and the packet payload.
3.2 Selection Process
* Selection Process
A Selection Process takes the Observed Packet Stream as its
input and selects a subset of that stream as its output.
* Selection State
A Selection Process may maintain state information for use by
the Selection Process. At a given time, the Selection State
may depend on packets observed at and before that time, and
other variables. Examples include:
(i) sequence numbers of packets at the input of Selectors;
(ii) a timestamp of observation of the packet at the
Observation Point;
(iii) iterators for pseudorandom number generators;
(iv) hash values calculated during selection;
(v) indicators of whether the packet was selected by a
given Selector;
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Selection Processes may change portions of the Selection State
as a result of processing a packet. Selection state for a
packet is to reflect the state after processing the packet.
* Selector
A Selector defines the action of a Selection Process on a
single packet of its input. If selected, the packet becomes an
element of the output Packet Stream.
The Selector can make use of the following information in
determining whether a packet is selected:
(i) the packet's content;
(ii) information derived from the packet's treatment at the
Observation Point;
(iii) any selection state that may be maintained by the
Selection Process.
* Composite Selector
A Composite Selector is an ordered composition of Selectors,
in which the output Packet Stream issuing from one Selector
forms the input Packet Stream to the succeeding Selector.
* Primitive Selector
A Selector is primitive if it is not a Composite Selector.
* Selection Sequence
From all the packets observed at an Observation Point, only a
few packets are selected by one or more Selectors. The
Selection Sequence is a unique value per Observation Domain
describing the Observation Point and the Selector IDs through
which the packets are selected.
3.3 Reporting
* Packet Reports
Packet Reports comprise a configurable subset of a packet's
input to the Selection Process, including the packet's
content, information relating to its treatment (for example,
the output interface), and its associated selection state (for
example, a hash of the packet's content)
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* Report Interpretation:
Report Interpretation comprises subsidiary information,
relating to one or more packets, that is used for
interpretation of their packet reports. Examples include
configuration parameters of the Selection Process.
* Report Stream:
The Report Stream is the output of a Metering Process,
comprising two distinguished types of information: Packet
Reports, and Report Interpretation.
3.4 Metering Process
A Metering Process selects packets from the Observed Packet
Stream using a Selection Process, and produces as output a
Report Stream concerning the selected packets. The PSAMP
Metering Process can be viewed as analogous to the IPFIX
metering process [IPFIX-PROTO], which produces flow records as
its output. While the Metering Process definition in this
document specifies the PSAMP definition, the PSAMP protocol
specifications [PSAMP-PROTO] will use the IPFIX Metering
Process definition, which also suits the PSAMP requirements.
The relationship between PSAMP and IPFIX is described more in
[PSAMP-INFO] and [PSAMP-PROTO].
3.5 Exporting Process
* Exporting Process:
An Exporting Process sends, in the form of Export Packet, the
output of one or more Metering Processes to one or more
Collectors.
* Export Packet:
An Export Packet is a combination of Report Interpretation
and/or one or more Packet Reports are bundled by the Exporting
Process into an Export Packet for exporting to a Collector.
3.6 PSAMP Device
* PSAMP Device
A PSAMP Device is a device hosting at least an Observation
Point, a Metering Process and an Exporting Process. Typically,
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corresponding Observation Point(s), Metering Process(es) and
Exporting Process(es) are co-located at this device, for
example at a router.
3.7 Collector
* Collector
A Collector receives a Report Stream exported by one or more
Exporting Processes. In some cases, the host of the Metering
and/or Exporting Processes may also serve as the Collector.
3.8 Selection Methods
* Filtering
A filter is a Selector that selects a packet deterministically
based on the Packet Content, or its treatment, or functions of
these occurring in the Selection State. Two examples are:
(i) Property match filtering: a packet is selected if a
specific field in the packet equals a predefined
value.
(ii) Hash-based selection: a hash function is applied to
the Packet Content, and the packet is selected if the
result falls in a specified range.
* Sampling
A selector that is not a filter is called a sampling
operation. This reflects the intuitive notion that if the
selection of a packet cannot be determined from its content
alone, there must be some type of sampling taking place.
Sampling operations can be divided into two subtypes:
(i) Content-independent sampling, which does not use
Packet Content in reaching sampling decisions.
Examples include systematic sampling, and uniform
pseudorandom sampling driven by a pseudorandom number
whose generation is independent of Packet Content.
Note that in Content-independent Sampling it is not
necessary to access the Packet Content in order to
make the selection decision.
(ii) Content-dependent sampling, in which the Packet
Content is used in reaching selection decisions. An
application is pseudorandom selection according to a
probability that depends on the contents of a packet
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field, e.g., sampling packets with a probability
dependent on their TCP/UDP port numbers. Note that
this is not a Filter.
* Hash Domain
A subset of the Packet Content and the packet treatment,
viewed as an N-bit string for some positive integer N.
* Hash Range
A set of M-bit strings for some positive integer M that define
the range of values the result of the hash operation can take.
* Hash Function
A deterministic map from the Hash Domain into the Hash Range.
* Hash Selection Range
A subset of the Hash Range. The packet is selected if the
action of the Hash Function on the Hash Domain for the packet
yields a result in the Hash Selection Range.
* Hash-based Selection
Filtering specified by a Hash Domain, a Hash Function, and
Hash Range and a Hash Selection Range.
* Approximative Selection
Selectors in any of the above categories may be approximated
by operations in the same or another category for the purposes
of implementation. For example, uniform pseudorandom Sampling
may be approximated by Hash-based Selection, using a suitable
Hash Function and Hash Domain. In this case, the closeness of
the approximation depends on the choice of Hash Function and
Hash Domain.
* Population
A Population is a Packet Stream, or a subset of a Packet
Stream. A Population can be considered as a base set from
which packets are selected. An example is all packets in the
Observed Packet Stream that are observed within some specified
time interval.
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* Population Size
The Population Size is the number of all packets in the
Population.
* Sample Size
The number of packets selected from the Population by a
Selector.
* Configured Selection Fraction
The Configured Selection Fraction is the ratio of the number
of packets selected by a Selector from an input Population, to
the Population Size, as based on the configured selection
parameters.
* Attained Selection Fraction
The Attained Selection Fraction is the actual ratio of the
number of packets selected by a Selector from an input
Population, to the Population Size.
For some sampling methods the Attained Selection Fraction can
differ from the Configured Selection Fraction due to, for
example, the inherent statistical variability in sampling
decisions of probabilistic Sampling and Hash-based Selection.
Nevertheless, for large Population Sizes and properly configured
Selectors, the Attained Selection Fraction usually approaches
the Configured Selection Fraction.
4. Categorization of Packet Selection Techniques
Packet selection techniques generate a subset of packets from an
Observed Packet Stream at an Observation Point. We distinguish
between Sampling and Filtering.
Sampling is targeted at the selection of a representative subset
of packets. The subset is used to infer knowledge about the
whole set of observed packets without processing them all. The
selection can depend on packet position, and/or on packet
content, and/or on (pseudo) random decisions.
Filtering selects a subset with common properties. This is used
if only a subset of packets is of interest. The properties can
be directly derived from the packet content, or depend on the
treatment given by the router to the packet. Filtering is a
deterministic operation. It depends on packet content or router
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treatment. It never depends on packet position or on (pseudo)
random decisions.
Note that a common technique to select packets is to compute a
Hash Function on some bits of the packet header and/or content
and to select it if the Hash Value falls in the Hash Selection
Range. Since hashing is a deterministic operation on the packet
content, it is a Filtering technique according to our
categorization. Nevertheless, Hash Functions are sometimes used
to emulate random Sampling. Depending on the chosen input bits,
the Hash Function and the Hash Selection Range, this technique
can be used to emulate the random selection of packets with a
given probability p. It is also a powerful technique to
consistently select the same packet subset at multiple
Observation Points [DuGr00]
The following table gives an overview of the schemes described
in this document and their categorization. An X in brackets (X)
denotes schemes for which also content-independent variants
exist. It easily can be seen that only schemes with both
properties, content dependence and deterministic selection, are
considered as filters.
Selection Scheme | Deterministic | Content- | Category
| Selection | dependent|
------------------------+---------------+----------+----------
Systematic | X | _ | Sampling
Count-based | | |
------------------------+---------------+----------+----------
Systematic | X | - | Sampling
Time-based | | |
------------------------+---------------+----------+----------
Random | - | - | Sampling
n-out-of-N | | |
------------------------+---------------+----------+----------
Random | - | - | Sampling
Uniform probabilistic | | |
------------------------+---------------+----------+----------
Random | - | (X) | Sampling
Non-uniform probabil. | | |
------------------------+---------------+----------+----------
Random | - | (X) | Sampling
Non-uniform flow-state | | |
------------------------+---------------+----------+----------
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Property Match | X | (X) | Filtering
Filtering | | |
------------------------+---------------+----------+----------
Hash Function | X | X | Filtering
------------------------+---------------+----------+----------
The categorization just introduced is mainly useful for the
definition of an information model describing Primitive
Selectors. More complex selection techniques can be described
through the composition of cascaded Sampling and Filtering
operations. For example, a packet selection that weights the
selection probability on the basis of the packet length can be
described as a cascade of a Filtering and a Sampling scheme.
However, this descriptive approach is not intended to be rigid:
if a common and consolidated selection practice turns out to be
too complex to be described as a composition of the mentioned
building blocks, an ad hoc description can be specified instead
and added as a new scheme to the information model.
5. Sampling
The deployment of Sampling techniques aims at the provisioning
of information about a specific characteristic of the parent
population at a lower cost than a full census would demand. In
order to plan a suitable Sampling strategy it is therefore
crucial to determine the needed type of information and the
desired degree of accuracy in advance.
First of all it is important to know the type of metric that
should be estimated. The metric of interest can range from
simple packet counts [JePP92] up to the estimation of whole
distributions of flow characteristics (e.g. packet
sizes)[ClPB93].
Secondly, the required accuracy of the information and with
this, the confidence that is aimed at, should be known in
advance. For instance for usage-based accounting the required
confidence for the estimation of packet counters can depend on
the monetary value that corresponds to the transfer of one
packet. That means that a higher confidence could be required
for expensive packet flows (e.g. premium IP service) than for
cheaper flows (e.g. best effort). The accuracy requirements for
validating a previously agreed quality can also vary extremely
with the customer demands. These requirements are usually
determined by the service level agreement (SLA).
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The Sampling method and the parameters in use must be clearly
communicated to all applications that use the measurement data.
Only with this knowledge a correct interpretation of the
measurement results can be ensured.
Sampling methods can be characterized by the Sampling algorithm,
the trigger type used for starting a Sampling interval and the
length of the Sampling interval. These parameters are described
here in detail. The Sampling algorithm describes the basic
process for selection of samples. In accordance to [AmCa89] and
[ClPB93] we define the following basic Sampling processes:
5.1 Systematic Sampling
Systematic Sampling describes the process of selecting the start
points and the duration of the selection intervals according to
a deterministic function. This can be for instance the periodic
selection of every k-th element of a trace but also the
selection of all packets that arrive at pre-defined points in
time. Even if the selection process does not follow a periodic
function (e.g. if the time between the Sampling intervals varies
over time) we consider this as systematic Sampling as long as
the selection is deterministic.
The use of systematic Sampling always involves the risk of
biasing the results. If the systematics in the Sampling process
resemble systematics in the observed stochastic process
(occurrence of the characteristic of interest in the network),
there is a high probability that the estimation will be biased.
Systematics in the observed process might not be known in
advance.
Here only equally spaced schemes are considered, where triggers
for Sampling are periodic, either in time or in packet count.
All packets occurring in a selection interval (either in time or
packet count) beyond the trigger are selected.
Systematic count-based
In systematic count-based Sampling the start and stop triggers
for the Sampling interval are defined in accordance to the
spatial packet position (packet count).
Systematic time-based
In systematic time-based Sampling time-based start and stop
triggers are used to define the Sampling intervals. All packets
are selected that arrive at the Observation Point within the
time-intervals defined by the start and stop triggers (i.e.
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arrival time of the packet is larger than the start time and
smaller than the stop time).
Both schemes are content-independent selection schemes. Content
dependent deterministic Selectors are categorized as filter.
5.2 Random Sampling
Random Sampling selects the starting points of the Sampling
intervals in accordance to a random process. The selection of
elements are independent experiments. With this, unbiased
estimations can be achieved. In contrast to systematic Sampling,
random Sampling requires the generation of random numbers. One
can differentiate two methods of random Sampling:
5.2.1 n-out-of-N Sampling
In n-out-of-N Sampling n elements are selected out of the parent
population that consists of N elements. One example would be to
generate n different random numbers in the range [1,N] and
select all packets which have a packet position equal to one of
the random numbers. For this kind of Sampling the Sample Size n
is fixed.
5.2.2 Probabilistic Sampling
In probabilistic Sampling the decision whether an element is
selected or not is made in accordance to a pre-defined selection
probability. An example would be to flip a coin for each packet
and select all packets for which the coin showed the head. For
this kind of Sampling the Sample Size can vary for different
trials. The selection probability does not necessarily has to be
the same for each packet. Therefore we distinguish between
uniform probabilistic Sampling (with the same selection
probability for all packets) and non-uniform probabilistic
Sampling (where the selection probability can vary for different
packets).
5.2.2.1 Uniform Probabilistic Sampling
For Uniform Probabilistic Sampling packets are selected
independently with a uniform probability p. This Sampling can be
count-driven, and is sometimes referred to as geometric random
Sampling, since the difference in count between successive
selected packets are independent random variables with a
geometric distribution of mean 1/p. A time-driven analog,
exponential random Sampling, has the time between triggers
exponentially distributed.
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Both geometric and exponential random Sampling are examples of
what is known as additive random Sampling, defined as Sampling
where the intervals or counts between successive samples are
independent identically distributed random variable.
5.2.2.2 Non-Uniform Probabilistic Sampling
This is a variant of Probabilistic Sampling in which the
Sampling probabilities can depend on the selection process
input. This can be used to weight Sampling probabilities in
order e.g. to boost the chance of Sampling packets that are rare
but are deemed important. Unbiased estimators for quantitative
statistics are recovered by renormalization of sample values;
see [HT52].
5.2.2.3 Non-Uniform Flow State Dependent Sampling
Another type of Sampling that can be classified as probabilistic
Non-Uniform is closely related to the flow concept as defined in
[RFC3917], and it is only used jointly with a flow monitoring
function (IPFIX metering process). Packets are selected,
dependent on a selection state. The point, here, is that the
selection state is determined also by the state of the flow the
packet belongs to and/or by the state of the other flows
currently being monitored by the associated flow monitoring
function. An example for such an algorithm is the "sample and
hold" method described in [EsVa01]:
- If a packet accounts for a flow record that already exists in
the IPFIX flow recording process, it is selected (i.e. the
flow record is updated)
- If a packet doesn't account to any existing flow record, it is
selected with probability p. If it has been selected a new
flow record has to be created.
A further algorithm that fits into the category of non-uniform
flow state dependent Sampling is described in [Moli03].
This type of Sampling is content dependent because the
identification of the flow the packet belongs to requires
analyzing part of the packet content. If the packet is selected,
then it is passed as an input to the IPFIX monitoring function
(this is called "Local Export" in [PSAMP-FW]. Selecting the
packet depending on the state of a flow cache is useful when
memory resources of the flow monitoring function are scarce
(i.e. there is no room to keep all the flows that have been
scheduled for monitoring).
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5.2.2.4 Configuration of non-uniform probabilistic and flow-state
Sampling
Many different specific methods can be grouped under the terms
non-uniform probabilistic and flow state Sampling. Dependent on
the Sampling goal and the implemented scheme, a different number
and type of input parameters is required to configure such
scheme.
Some concrete proposals for such methods exist from the research
community (e.g. [EsVa01],[DuLT01],[Moli03]). Some of these
proposals are still in an early stage and need further
investigations to prove their usefulness and applicability. It
is not our aim to indicate preference amongst these methods.
Instead, we only describe here the basic methods and leave the
specification of explicit schemes and their parameters up to
vendors (e.g. as extension of the information model).
6. Filtering
Filtering is the deterministic selection of packets based on the
packet content, the treatment of the packet at the Observation
Point, or deterministic functions of these occurring in the
selection state. The packet is selected if these quantities fall
into a specified range. The role of Filtering, as the word
itself suggest, is to separate all the packets having a certain
property from those not having it. A distinguishing
characteristic from Sampling is that the selection decision does
not depend on the packet position in time or in the space, or on
a random process.
We identify and describe in the following two Filtering
techniques.
6.1 Property Match Filtering
With this Filtering method a packet is selected if specific
fields within the packet and/or properties of the router state
equal a predefined value. Possible filter fields are all IPFIX
flow attributes specified in [IPFIX-INFO]. Further fields can be
defined by vendor specific extensions.
A packet is selected if Field=Value. Masks and ranges are only
supported to the extent to which [IPFIX-INFO] allows them e.g.
by providing explicit fields like the netmasks for source and
destination addresses.
AND operations are possible by concatenating filters, thus
producing a composite selection operation. In this case, the
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ordering in which the filtering happens is implicitly defined
(outer filters come after inner filters). However, as long as
the concatenation is on filters only, the result of the cascaded
filter is independent from the order, but the order may be
important for implementation purposes, as the first filter will
have to work at a higher rate. In any case, an implementation
is not constrained to respect the filter ordering, as long as
the result is the same, and it may even implement the composite
filtering in filtering in one single step.
OR operations are not supported with this basic model. More
sophisticated filters (e.g. supporting bitmasks, ranges or OR
operations etc.) can be realized as vendor specific schemes.
Property match operations should be available for different
protocol portions of the packet header:
(i) the IP header (excluding options in IPv4, stacked
headers in IPv6)
(ii) transport header
(iii) encapsulation headers (e.g. the MPLS label stack, if
present)
When the PSAMP Device offers property match filtering, and, in
its usual capacity other than in performing PSAMP functions,
identifies or processes information from IP, transport or
encapsulation protocols, then the information should be made
available for filtering. For example, when a PSAMP Device
routes based on destination IP address, that field should be
made available for filtering. Conversely, a PSAMP Device that
does not route is not expected to be able to locate an IP
address within a packet, or make it available for Filtering,
although it may do so.
Since packet encryption conceals the real values of encrypted
fields, property match filtering must be configurable to ignore
encrypted packets, when detected.
The Selection Process may support filtering based on the
properties of the router state:
(i) Ingress interface at which packet arrives equals a
specified value
(ii) Egress interface to which packet is routed to equals a
specified value
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(iii) Packet violated Access Control List (ACL) on the
router
(iv) Failed Reverse Path Forwarding (RPF)
(v) Failed Resource Reservation (RSVP)
(vi) No route found for the packet
(vii) Origin Border Gateway Protocol (BGP) Autonomous System
(AS) [RFC4271] equals a specified value or lies within
a given range
(viii)Destination BGP AS equals a specified value or lies
within a given range
Router architectural considerations may preclude some
information concerning the packet treatment being available at
line rate for selection of packets. For example, the Selection
Process may not be implemented in the fast path that is able to
access routing state at line rate. However, when filtering
follows sampling (or some other selection operation) in a
Composite Selector, the rate of the Packet Stream output from
the sampler and input to the filter may be sufficiently slow
that the filter could select based on routing state.
6.2 Hash-based Filtering
A Hash Function h maps the Packet Content c, or some portion of
it, onto a Hash Range R. The packet is selected if h(c) is an
element of S, which is a subset of R called the Hash Selection
Range. Thus Hash-based Selection is indeed a particular case of
Filtering: the object is selected if c is in inv(h(S)). But for
desirable Hash Functions the inverse image inv(h(S)) will be
extremely complex, and hence h would not be expressible as, say,
a Property Match Filter or a simple combination of these.
Hash-based selection has mainly two types of usage: it offers a
way to approximate random Sampling by using packet content to
generate pseudorandom variates, and a way to consistently select
subsets of packets that share a common property (e.g. at
different Observation Points).
In the following subsections we give more details about them.
However, both usages require that the Hash Functions has two
statistical properties.
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First, the Hash Function h must have good mixing properties, in
the sense that small changes in the input (e.g. the flipping of
a single bit) cause large changes in the output (many bits
change). Then any local clump of values of c is spread widely
over R by h, and so the distribution of h(c) is fairly uniform
even if the distribution of c is not. Then the Sampling Fraction
is #S/#R, which can be tuned by choice of S.
The second desirable property depends more closely on the
statistics of the content c. In applications, the content c
comprises a number of distinct fields, c1 ... cm, e.g. source
and destination IP Address, IP identification, and TCP/UDP port
numbers (if present) for a packet. With a Hash Function
satisfying the first properties above, selection decisions will
appear uncorrelated with the contents of any individual field,
if the complementary fields are (i) sufficiently variable
themselves, and (ii) sufficiently uncorrelated with cj.
6.2.1 Application Examples for Hash-based Selection
6.2.1.1 Approximation of Random Sampling
Although pseudorandom number generators with well understood
properties have been developed, they may not be the method of
choice in settings where computational resources are scarce. A
convenient alternative is to use Hash Functions of packet
content as a source of randomness. The hash (suitably
renormalized) is a pseudorandom variate in the interval [0,1].
Other schemes may use packet fields in iterators for
pseudorandom numbers. However, the statistical properties of an
ideal packet selection law (such as independent Sampling for
different packets, or independence on packet content) may not be
exactly rendered by an implementation, but only approximately
so.
Use of packet content to generate pseudorandom variates shares
with Non-uniform Probabilistic Sampling (see Section 3.1.2.2.2
above) the property that selection decisions depend on Packet
Content. However, there is a fundamental difference between the
two. In the former case the content determines pseudorandom
variates. In the latter case the content only determines the
selection probabilities: selection could then proceed e.g by use
of random variates obtained by an independent pseudorandom
number generator.
6.2.1.2 Trajectory Sampling and Consistent Packet Selection.
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Trajectory Sampling is the consistent selection of a subset of
packets at either all of a set of Observation Points or none of
them. Trajectory Sampling is realized by Hash-based Selection if
all Observation Points in the set use a common Hash Function,
Hash Domain and selection range. The Hash Domain comprises all
or part of the packet content that is invariant along the packet
path. Fields such as Time-to-Live, which is decremented per hop,
and header CRC, which is recalculated per hop, are thus excluded
from the Hash Domain. The Hash Domain needs to be wider than
just a flow key, if packets are to be selected quasirandomly
within flows.
The trajectory (or path) followed by a packet is reconstructed
from PSAMP reports on it that reach a Collector. Reports on a
given packet originating from different observations points are
associated by matching a label from the reports. The label may
comprise that portion invariant packet content that is reported,
or possibly some digest of the invariant packet content that is
inserted into the packet report at the Observation Point. Such a
digest may be constructed by applying a second Hash Function
(distinct from that used for selection) to the invariant packet
content. The reconstruction of trajectories, and methods for
dealing with possible ambiguities due to label collisions
(identical labels reported for different packets) and potential
loss of reports in transmission, are dealt with in [DuGr00],
[DuGG02] and [DuGr04].
Applications of trajectory Sampling include (i) estimation of
the network path matrix, i.e., the traffic intensities according
to network path, broken down by flow key; (ii) detection of
routing loops, as indicated by self-intersecting trajectories;
(iii) passive performance measurement: prematurely terminating
trajectories indicate packet loss, packet one way delay can be
determined if reports include (synchronized) timestamps of
packet arrival at the Observation Point; (iv) network attack
tracing, of the actual paths taken by attack packets with
spoofed source addresses.
6.2.2 Security Considerations for Hash Functions
A concern for Hash-based Selection is whether some large set of
related packets could be disproportionately sampled, i.e., have
an Attained Sampling Fraction significantly different from the
Configured Sampling Fraction, either (i) through unanticipated
behavior in the Hash Function, or (ii) because the packets had
been deliberately crafted to have this property.
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The first point underlines the importance of using a Hash
Function with good mixing properties. The statistical properties
of candidate Hash Functions need to be evaluated, preferably on
packet traces before adoption for hash-based Sampling. However,
hash functions which perform well on typical traffic may not be
sufficiently strong to withstand attacks specifically targeted
against them. As detailed in the following section, only
cryptographic hash functions employing a private parameter
operating in pseudo-random function mode are sufficiently strong
to
withstand the range of conceivable attacks. For example, fixed
or
variable length inputs could be hashed using a block cipher
(like AES) in cipher-block-chaining mode. Fixed length inputs
could also be hashed using an iterated cryptographic hash
function (like MD5 or SHA1), with a private initial vector. For
variable length inputs, iterated cryptographic hash function
(like MD5 or SHA1) should employ private string post-pended to
the data in addition to a private initial vector. For more
details, see the "append-cascade" construction of [BeCK96].
The following assumes that the hash function is public and hence
known to an attacker. An attacker uses its knowledge of the hash
function to craft packets which are then dispatched, either as
the attack itself, or to elicit further information which can be
used to refine the attack. Thus two scenarios are considered. In
the first scenario, the attacker has no knowledge about whether
the crafted packets are selected or not. In the second scenario
the attacker uses some knowledge of sampling outcomes; the means
by which this might be acquired is discussed below. Some attacks
that involve tampering with export packets in transit, as
opposed to attacking the PSAMP device, are discussed in
[GoRe07].
6.2.2.1 Vulnerabilities of Hash-based selection without knowledge
of selection outcomes.
(i) The hash function does not use a private parameter.
If the selection range is public, an attacker can craft packets
whose selection properties are known in advance. If the
selection range is private, an attacker cannot determine whether
a crafted packet is selected. However by computing the hash on
different trial crafted packets, and selecting those yielding a
given hash value, the attacker can construct an arbitrarily
large set of distinct packets with a common selection
properties, i.e., packets that will be either all selected or
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all not selected. This can be done whatever the strength of the
hash function.
(ii) The hash function is not cryptographically strong.
If the hash function is not cryptographically strong, it may
still be possible to construct sequences of distinct packets
with the common selection property. An example is the standard
CRC-32 hash function used with a private modulus (but without a
private string post-pended to the input). It has weak mixing
properties for low order bits. Consequently, simply by
incrementing the hash input, one obtains distinct packets whose
hashes mostly fall in a narrow range, and hence are likely
commonly selected; see [GoRe07]
Suitable parameterization of the hash function can make such
attacks more difficult. For example, post-pending a private
string to the input before hashing with CRC-32 will give
stronger mixing properties over all bits of the input. However,
with a hash function, such as CRC-32, that is not
cryptographically strong, the possibility of discovering a
method to construct packet sets with the common selected
property cannot be ruled out, even when a private modulus or
post-pended string is used.
6.2.2.2 Vulnerabilities of Hash-based selection using knowledge of
selection outcomes.
Knowledge of the selection outcomes of crafted packets can by
used by an attacker to more easily construct sets of packets
which are disproportionately sampled and/or are commonly
selected. There are several ways an attacker might acquire this
knowledge:
(i) Billing Reports: if samples are used for billing purposes,
then the selection outcomes of packets may be able to be
inferred by correlating a crafted packet stream with the billing
reports that it generates. However, the rate at knowledge of
selection outcomes can be acquired depends on the temporal and
spatial granularity of the billing reports, being slower the
more aggregated the reports are.
(ii) Feedback from an Intrusion Detection System: e.g., a
botmaster adversary learns if his packets were detected by the
intrusion detection system by seeing if one of his bots is
blocked by the network.
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(iii) Observation of the Report Stream: export packets sent
across a public network may be eavesdropped on by an adversary.
Encryption of the export packets provides only a partial
defense, since it may be possible to infer the selection
outcomes of packets by correlating a crafted packet stream with
the occurrence (not the content) of packets in the export stream
that it generates. The rate at which such knowledge could be
acquired is limited by the temporal resolution at which reports
can be associated with packets, e.g. due to processing and
propagation variability, and difficulty in distinguishing report
on attack packets from those of background traffic, if present.
The association between packets and their reports on which this
depends could be removed by padding export packets to a constant
length and sending them at a constant rate.
We now turn to attacks that can exploit knowledge of selection
outcomes. Firstly, with a non-cryptographic hash function,
knowledge of selection outcomes for a trial stream may be used
to further craft a packet set with the common selection
property. This has been demonstrated for the modular hash f(x) =
a x + b mod k, for private parameters a, b, and k. With sampling
rate p, knowledge of the sampling outcomes of roughly 2/p is
sufficient for the attack to succeed, independent of the values
of a, b and k. With knowledge of the selection outcomes of a
larger number of packets, the parameters a b and k can be
determined; see [GoRe07].
A cryptographic hash function employing a private parameter and
operating in one of the pseudo-random function modes specified
above is not vulnerable to these attacks, even if the selection
range is known.
6.2.2.3 Vulnerabilities to Replay Attacks
Since hash-based selection is deterministic, any packet or set
of packets with known selection properties can be replayed into
a network and experience the same selection outcomes provide the
hash function and its parameters are not changed. Repetition of
a single packet may be noticeable to other measurement methods
if employed (e.g. collection of flow statistics), whereas a set
of distinct packets that appears statistically similar to
regular traffic may be less noticeable.
Replay attacks may be mitigated by repeated changing of hash
function parameters. This also prevents attacks that exploit
knowledge of sampling outcomes, at least if the parameters are
changed at least as fast as the knowledge can be acquired by an
attacker. In order to preserve the ability to perform Trajectory
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Sampling, parameter changed would have to be simultaneous (or
approximately so) across all observation point.
6.2.3 Choice of Hash-Function
The specific choice of hash function represents a trade-off
between complexity and ease of implementation. Ideally, a
cryptographically strong hash function employing a private
parameter and operating in pseudorandom function mode as
specified above would be used, yielding a good emulation a
random packet selection at a target sampling rate, and giving
maximal robustness against the attacks described in the previous
section. However, it is not assumed that all PSAMP devices will
be capable of applying a cryptographically strong hash function
to every packet at line rate. For this reason, the hash
functions listed in this section will be of a weaker variety.
Future protocol extensions that employ stronger hash functions
are not precluded.
6.2.3.1 Properties of some hash functions.
This document recommends 3 hash functions: IPSX, BOB and CRC-32.
Specifications of IPSX and BOB are in the appendix; the CRC-32
function is described in [RFC1071]. None of these hash functions
is recommended for cryptographic purposes. A comparison of hash-
functions with regard to execution speed, collision probability,
uniformity of the distribution of values in the Hash
Range and the speed of the functions is described in [MoND05].
(i) Speed: IPSX is simple to implement and was correspondingly
about an order of magnitude faster to execute per packet than
BOB or CRC-32.
(ii) Uniformity: All three hash functions evaluated showed
relatively poor uniformity with 16 byte input that was drawn
from only invariant fields in the IP and TCP/UDP headers (i.e.
header fields that do not change from hop to hop). IPSX is
inherently limited to 16 bytes. BOB and
CRC-32 exhibits noticeably better uniformity when 4 or more
bytes from the payload are also included in the input. Although
the uniformity has been checked for different traffic traces,
results cannot be generalized to arbitrary traffic. Since hash-
based selection is a deterministic function on the packet
content, it can always be biased towards packets with specific
attributes. Furthermore, it should be noted that all Hash
Functions were evaluated only for IPv4.
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6.2.3.2 Hash Functions for Packet Selection
The BOB function SHOULD be used for packet selection operations.
Both the parameter (the init value) and the selection range
should be kept private. Other functions, such as CRC-32 and IPSX
MAY be used. If CRC-32 is used, the input should first be post-
pended with a private string that acts as a parameter, and the
modulus of the CRC should also be kept private.
Input bytes for the Hash Function need to be invariant along the
path the packet is traveling. Only with this it is ensured that
the same packets are selected at different observation points.
Furthermore they should have a high variability between
different packets to generate a high variation in the Hash
Range.
If a hash-based selection with the BOB function is used with
IPv4 traffic, the following input bytes MUST be used.
- IP identification field
- Flags field
- Fragment offset
- Source IP address
- Destination IP address
- A configurable number of bytes from the IP payload, starting
at a configurable offset.
All investigated Hash Functions were evaluated only for IPv4.
Due to the IPv6 header fields and address structure it is
expected that there is less randomness in IPv6 packet headers
than in IPv4 headers. Nevertheless, the randomness of IPv6
traffic was not evaluated in the tests mentioned above. In
addition to this, IPv6 traffic profiles may change significantly
in future when IPv6 is used by a broader community. If a hash-
based selection with the BOB function is used with IPv6 traffic,
the following input bytes MUST be used.
- Payload length (2 bytes)
- Byte number 10,11,14,15,16 of the IPv6 source address
- Byte number 10,11,14,15,16 of the IPv6 destination address
- A configurable number of bytes from the IP payload, starting
at a configurable offset. It is recommended to use at least 4
bytes from the IP payload.
The payload itself is not changing during the path. Even if some
routers process some extension headers they are not going to
strip them from the packet. Therefore the payload length is
invariant along the path. Furthermore it usually differs for
different packets. The IPv6 address has 16 bytes. The first part
is the network part and it contains low variation. The second
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part is the host part and contains higher variation. Therefore
the second part of the address is used. Nevertheless, the
uniformity has not been checked for IPv6 traffic.
6.2.3.3 Hash Functions Suitable for Packet Digesting
For digesting Packet Content for inclusion in a reported label,
the most important property is a low collision frequency. A
secondary requirement is the ability to accept variable length
input, in order to allow inclusion of maximal amount of packet
as input. Execution speed is of secondary importance, since the
digest need only be formed from selected packets.
For this purpose also the BOB function is recommended. Other
functions (such as CRC-32) MAY be used. Among the functions
capable of operating with variable length input BOB and CRC-32
have the fastest execution, BOB being slightly faster. IPSX is
not recommended for digesting because it has a significantly
higher collision rate and takes only a fixed length input.
7. Parameters for the Description of Selection Techniques
This section gives an overview of different alternative
selection schemes and their required parameters. In order to be
compliant with PSAMP at least one of proposed schemes MUST be
implemented.
The decision whether to select a packet or not is based on a
function which is performed when the packet arrives at the
selection process. Packet selection schemes differ in the input
parameters for the selection process and the functions they
require to do the packet selection. The following table gives an
overview.
Scheme | input parameters | functions
---------------+------------------------+-------------------
systematic | packet position | packet counter
count-based | Sampling pattern |
---------------+------------------------+-------------------
systematic | arrival time | clock or timer
time-based | Sampling pattern |
---------------+------------------------+-------------------
random | packet position | packet counter,
n-out-of-N | Sampling pattern | random numbers
| (random number list) |
---------------+------------------------+-------------------
uniform | Sampling | random function
probabilistic | probability |
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---------------+------------------------+-------------------
non-uniform |e.g. packet position, | selection function,
probabilistic | packet content(parts) | probability calc.
---------------+------------------------+-------------------
non-uniform |e.g. flow state, | selection function,
flow-state | packet content(parts) | probability calc.
---------------+------------------------+-------------------
property | packet content(parts) | filter function or
match | or router state | state discovery
---------------+------------------------+-------------------
hash-based | packet content(parts) | Hash Function
---------------+------------------------+-------------------
7.1 Description of Sampling Techniques
In this section we define what elements are needed to describe
the most common Sampling techniques. Here the selection function
is pre-defined and given by the Selector ID.
Sampler Description:
SELECTOR_ID
SELECTOR_TYPE
SELECTOR_PARAMETERS
Where:
SELECTOR_ID:
Unique ID for the packet sampler.
SELECTOR_TYPE
For Sampling processes the SELECTOR TYPE defines what Sampling
algorithm is used.
Values: Systematic Count-based | Systematic Time-based | Random
n-out-of-N | Uniform Probabilistic | Non-uniform Probabilistic |
Non-uniform Flow-state
SELECTOR_PARAMETERS
For Sampling processes the SELECTOR PARAMETERS define the input
parameters for the process. Interval length in systematic
Sampling means, that all packets that arrive in this interval
are selected. The spacing parameter defines the spacing in time
or number of packets between the end of one Sampling interval
and the start of the next succeeding interval.
Case n out of N:
- Population size N, Sample size n
Case Systematic Time Based:
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- Interval length (in usec), Spacing (in usec)
Case Systematic Count Based:
- Interval length(in packets), Spacing (in packets)
Case Uniform Probabilistic (with equal probability per packet):
- Sampling probability p
Case Non-uniform Probabilistic:
- Calculation function for Sampling probability p (see also
section .
5.2.2.4)
Case flow state:
- Information reported for flow state sampling are not
defined in this document (see also section 5.2.2.4)
7.2 Description of Filtering Techniques
In this section we define what elements are needed to describe
the most common Filtering techniques. The structure closely
parallels the one presented for the Sampling techniques.
Filter Description:
SELECTOR_ID
SELECTOR_TYPE
SELECTOR_PARAMETERS
Where:
SELECTOR_ID:
Unique ID for the packet filter. The ID can be calculated under
consideration of the SELECTION SEQUENCE and a local ID.
SELECTOR_TYPE
For Filtering processes the SELECTOR TYPE defines what Filtering
type is used.
Values: Matching | Hashing | Router_state
SELECTOR_PARAMETERS
For Filtering processes the SELECTOR PARAMETERS define formally
the common property of the packet being filtered. For the
filters of type Matching and Hashing the definitions have a lot
of points in common.
Values:
Case Matching
- Information Element (from [IPFIX-INFO])
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- Value (type in accordance to [IPFIX-INFO])
In case of multiple match criteria, multiple "case matching"
have to be bound by a logical AND.
Case Hashing:
- Hash Domain (Input bits from packet)
- <Header type = ipv4>
- <Input bit specification, header part>
- <Header type = ipv6>
- <Input bit specification, header part>
- <payload byte number N>
- <Input bit specification, payload part>
- Hash Function
- Hash function name
- Length of input key (eliminate 0x bytes)
- Output value (length M and bitmask)
- Hash Selection Range, as a list of non overlapping
intervals [start value, end value] where value is in
[0,2^M-1]
- Additional parameters dependent on specific Hash
Function (e.g. hash input bits (seed))
Notes to input bits for Case Hashing:
- Input bits can be from header part only, from the payload
part only or from both.
- The bit specification, for the header part, can be
specified for ipv4 or ipv6 only, or both
- In case of ipv4, the bit specification is a sequence of 20
Hexadecimal numbers [00,FF] specifying a 20 bytes bitmask
to be applied to the header.
- In case of ipv6, it is a sequence of 40 Hexadecimal numbers
[00,FF] specifying a 40 bytes bitmask to be applied to the
header
- The bit specification, for the payload part, is a sequence
of Hexadecimal numbers [00,FF] specifying the bitmask to be
applied to the first N bytes of the payload, as specified
by the previous field. In case the Hexadecimal number
sequence is longer then N, only the first N numbers are
considered.
- In case the payload is shorter than N, the Hash Function
cannot be applied. Other options, like padding with zeros,
may be considered in the future.
- A Hash Function cannot be defined on the options field of
the ipv4 header, neither on stacked headers of ipv6.
- The Hash Selection Range defines a range of hash-values
(out of all possible results of the Hash-Operation). If the
hash result for a specific packet falls in this range, the
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packet is selected. If the value is outside the range, the
packet is not selected. E.g. if the selection interval
specification is [1:3], [6:9] all packets are selected for
which the hash result is 1,2,3,6,7,8, or 9. In all other
cases the packet is not selected.
Case Router State:
- Ingress interface at which the packet arrives equals a
specified value
- Egress interface to which the packet is routed equals a
specified value
- Packet violated Access Control List (ACL) on the router
- Reverse Path Forwarding (RPF) failed for the packet
- Resource Reservation is insufficient for the packet
- No route found for the packet
- Origin AS equals a specified value or lies within a given
range
- Destination AS equals a specified value or lies within a
given range
Note to Case Router State:
- All Router state entries can be linked by AND operators
8. Composite Techniques
Composite schemes are realized by combining the selector IDs
into a Selection Sequence. The Selection Sequence contains all
selector IDs that are applied to the packet stream subsequently.
Some examples of composite schemes are reported below.
8.1 Cascaded Filtering->Sampling or Sampling->Filtering
If a filter precedes a Sampling process the role of Filtering is
to create a set of "parent populations" from a single stream
that can then be fed independently to different Sampling
functions, with different parameters tuned for the population
itself (e.g. if streams of different intensity result from
Filtering, it may be good to have different Sampling rates). If
Filtering follows a Sampling process, the same Sampling Fraction
and type is applied to the whole stream, independently of the
relative size of the streams resulting from the Filtering
function. Moreover, also packets not destined to be selected in
the Filtering operation will "load" the Sampling function. So,
in principle, Filtering before Sampling allows a more accurate
tuning of the Sampling procedure, but if filters are too complex
to work at full line rate (e.g. because they have to access
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router state information), Sampling before Filtering may be a
need.
8.2 Stratified Sampling
Stratified Sampling is one example for using a composite
technique. The basic idea behind stratified Sampling is to
increase the estimation accuracy by using a-priori information
about correlations of the investigated characteristic with some
other characteristic that is easier to obtain. The a-priori
information is used to perform an intelligent grouping of the
elements of the parent population. In this manner, a higher
estimation accuracy can be achieved with the same Sample Size or
the Sample Size can be reduced without reducing the estimation
accuracy.
Stratified Sampling divides the Sampling process into multiple
steps. First, the elements of the parent population are grouped
into subsets in accordance to a given characteristic. This
grouping can be done in multiple steps. Then samples are taken
from each subset.
The stronger the correlation between the characteristic used to
divide the parent population (stratification variable) and the
characteristic of interest (for which an estimate is sought
after), the easier is the consecutive Sampling process and the
higher is the stratification gain. For instance, if the dividing
characteristic were equal to the investigated characteristic,
each element of the sub-group would be a perfect representative
of that characteristic. In this case it would be sufficient to
take one arbitrary element out of each subgroup to get the
actual distribution of the characteristic in the parent
population. Therefore stratified Sampling can reduce the costs
for the Sampling process (i.e. the number of samples needed to
achieve a given level of confidence).
For stratified Sampling one has to specify classification rules
for grouping the elements into subgroups and the Sampling scheme
that is used within the subgroups. The classification rules can
be expressed by multiple filters. For the Sampling scheme within
the subgroups the parameters have to be specified as described
above. The use of stratified Sampling methods for measurement
purposes is described for instance in [ClPB93] and [Zseb03].
9. Security Considerations
Security considerations concerning the choice of sampling hash
function have been discussed in Section 6.2.2. That section
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Internet Draft Techniques for IP Packet Selection June 2007
discussed a number of potential attacks to craft packet streams
which are disproportionately detected and/or discover the hash
function parameters, the vulnerabilities of different hash
functions to these attacks, and practices to minimize these
vulnerabilities. In addition to this a user can gains knowledge
about the start and stop triggers in time-based systematic
sampling e.g. by sending test packets. This knowledge might
allow users to modify their send schedule in a way that their
packets are disproportionately selected or not selected
[GoRe07].
Further security threats can occur if the configuration of
Sampling parameters or the communication of Sampling parameters
to the application is corrupted. This document only describes
Sampling schemes that can be used for packet selection. It
neither describes a mechanism how those parameters are
configured nor how these parameters are communicated to the
application. Therefore the security threats that originate from
this kind of communication cannot be assessed with the
information given in this document.
10. Acknowledgements
We would like to thank the PSAMP group, especially Benoit Claise
and Stewart Bryant, for fruitful discussions and for
proofreading the document. We thank Sharon Goldberg for her
input on security issues concerning hash-based selection.
11. IANA Considerations
This document has no actions for IANA.
12. Normative References
[RFC2119] Bradner, S., Key words for use in RFCs to Indicate
Requirement Levels, BCP 14, RFC 2119, March 1997
13. Informative References
[AmCa89] Paul D. Amer, Lillian N. Cassel, "Management of
Sampled Real-Time Network Measurements", 14th
Conference on Local Computer Networks, October
1989, Minneapolis, pages 62-68, IEEE, 1989.
[BeCK96] M. Bellare, R. Canetti and H. Krawczyk,
"Pseudorandom Functions Revisited: The Cascade
Construction and its Concrete Security", Symposium
on Foundations of Computer Science, 1996.
Zseby, Molina, Duffield, Niccolini, Raspall [Page 33]
Internet Draft Techniques for IP Packet Selection June 2007
[ClPB93] K.C. Claffy, George C. Polyzos, Hans-Werner Braun,
"Application of Sampling Methodologies to Network
Traffic Characterization", Proceedings of ACM
SIGCOMM'93, San Francisco, CA, USA, September 13 -
17, 1993.
[DuGG02] N.G. Duffield, A. Gerber, M. Grossglauser,
"Trajectory Engine: A Backend for Trajectory
Sampling", IEEE Network Operations and Management
Symposium 2002, Florence, Italy, April 15-19, 2002.
[DuGr00] N.G. Duffield, M. Grossglauser, "Trajectory
Sampling for Direct Traffic Observation",
Proceedings of ACM SIGCOMM 2000, Stockholm, Sweden,
August 28 - September 1, 2000.
[DuGr04] N. G. Duffield and M. Grossglauser "Trajectory
Sampling with Unreliable Reporting", Proc IEEE
Infocom 2004, Hong Kong, March 2004.
[DuLT01] N.G. Duffield, C. Lund, and M. Thorup, "Charging
from Sampled Network Usage", ACM Internet
Measurement Workshop IMW 2001, San Francisco, USA,
November 1-2, 2001.
[EsVa01] C. Estan and G. Varghese, "New Directions in
Traffic Measurement and Accounting", ACM SIGCOMM
Internet Measurement Workshop 2001, San Francisco
(CA) Nov. 2001.
[GoRe07] S. Goldberg, J. Rexford, "Security Vulnerabilities
and Solutions for Packet Sampling", IEEE Sarnoff
Symposium, Princeton, NJ, May 2007.
[HT52] D.G. Horvitz and D.J. Thompson, "A Generalization
of Sampling without replacement from a Finite
Universe" J. Amer. Statist. Assoc. Vol. 47, pp.
663-685, 1952.
[IPFIX-INFO] J. Meyer, J. Quittek, S. Bryant "Information Model
for IP Flow Information Export", RFC XXXX
[Currently Internet Draft, draft-ietf-ipfix-info-
15, February 2007].
[IPFIX-PROTO]B. Claise (Editor) "Specification of the IPFIX
Protocol for the Exchange of IP Traffic Flow
Information", RFC XXXX. [Currently Internet Draft,
draft-ietf-ipfix-protocol-24.txt, November 2006].
Zseby, Molina, Duffield, Niccolini, Raspall [Page 34]
Internet Draft Techniques for IP Packet Selection June 2007
[Jenk97] B. Jenkins, "Algorithm Alley", Dr. Dobb's Journal,
September 1997.
http://burtleburtle.net/bob/hash/doobs.html
[JePP92] Jonathan Jedwab, Peter Phaal, Bob Pinna, "Traffic
Estimation for the Largest Sources on a Network,
Using Packet Sampling with Limited Storage", HP
technical report, Managemenr, Mathematics and
Security Department, HP Laboratories, Bristol,
March 1992,
http://www.hpl.hp.com/techreports/92/HPL-92-35.html
[Moli03] M.Molina, "A scalable and efficient methodology for
flow monitoring in the Internet", International
Teletraffic Congress (ITC-18), Berlin, Sep. 2003
[MoND05] M. Molina, S.Niccolini, N.G.Duffield "A Comparative
Experimental Study of Hash Functions Applied to
Packet Sampling" International Teletraffic Congress
(ITC-19), Beijing, August 2005.
[PSAMP-FW] Nick Duffield (Ed.), "A Framework for Packet
Selection and Reporting", RFC XXXX [currently
Internet Draft draft-ietf-psamp-framework-11, work
in progress, May 2007].
[PSAMP-INFO] T. Dietz, F. Dressler, G. Carle, B. Claise,
"Information Model for Packet Sampling Exports",
RFC XXXX. [Currently Internet Draft, draft-ietf-
psamp-info-06, June 2007]
[PSAMP-PROTO] B. Claise (Ed.), "Packet Sampling (PSAMP) Protocol
Specifications", RFC XXXX. [Currently Internet
Draft draft-ietf-psamp-protocol-07.txt, work in
progress, October 2006].
[RFC1071] R. Braden, D. Borman, C. Partridge, "Computing the
Internet Checksum", RFC 1071, Sep. 1988 (updated by
RFCs1141 and RFC1624).
[RFC3917] J. Quittek, T. Zseby, B. Claise, S. Zander,
"Requirements for IP Flow Information Export", RFC
3917, October 2004.
[RFC4271] Y. Rekhter, T. Li, S. Hares, "A Border Gateway
Protocol 4 (BGP-4)", RFC 4271, January 2006.
Zseby, Molina, Duffield, Niccolini, Raspall [Page 35]
Internet Draft Techniques for IP Packet Selection June 2007
[Zseb03] T. Zseby, "Stratification Strategies for Sampling-
based Non-intrusive Measurement of One-way Delay",
Proceedings of Passive and Active Measurement
Workshop (PAM 20003), La Jolla, CA, USA, pp. 171-
179, April 2003.
Authors' Addresses
Tanja Zseby
Fraunhofer Institute for Open Communication Systems
Kaiserin-Augusta-Allee 31
10589 Berlin
Germany
Phone: +49-30-34 63 7153
Email: tanja.zseby@fokus.fraunhofer.de
Maurizio Molina
DANTE
City House
126-130 Hills Road
Cambridge CB21PQ
United Kingdom
Phone: +44 1223 371 300
Email: maurizio.molina@dante.org.uk
Nick Duffield
AT&T Labs - Research
Room B-139
180 Park Ave
Florham Park NJ 07932, USA
Phone: +1 973-360-8726
Email: duffield@research.att.com
Saverio Niccolini
Network Laboratories, NEC Europe Ltd.
Kurfuerstenanlage 36
69115 Heidelberg
Germany
Phone: +49-6221-9051118
Email: saverio.niccolini@netlab.nec.de
Fredric Raspall
EPSC-UPC
Dept. of Telematics
Av. del Canal Olimpic, s/n
Edifici C4
E-08860 Castelldefels, Barcelona
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Spain
Email: fredi@entel.upc.es
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This document and the information contained herein are provided
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ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
Zseby, Molina, Duffield, Niccolini, Raspall [Page 37]
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ANY WARRANTY THAT THE USE OF THE INFORMATION HEREIN WILL NOT
INFRINGE ANY RIGHTS OR ANY IMPLIED WARRANTIES OF MERCHANTABILITY
OR FITNESS FOR A PARTICULAR PURPOSE.
Appendix A: Hash Functions
A.1 IP Shift-XOR (IPSX) Hash Function
The IPSX Hash Function is tailored for acting on IP version 4
packets. It exploits the structure of IP packet and in
particular the variability expected to be exhibited within
different fields of the IP packet in order to furnish a hash
value with little apparent correlation with individual packet
fields. Fields from the IPv4 and TCP/UDP headers are used as
input. The IPSX Hash Function uses a small number of simple
instructions.
Input parameters: None
Built-in parameters: None
Output: The output of the IPSX is a 16 bit number
Functioning:
The functioning can be divided into two parts: input selection,
which forms are composite input from various portions of the IP
packet, followed by computation of the hash on the composite.
Input Selection:
The raw input is drawn from the first 20 bytes of the IP packet
header and the first 8 bytes of the IP payload. If IP options
are not used, the IP header has 20 bytes, and hence the two
portions adjoin and comprise the first 28 bytes of the IP
packet. We now use the raw input as 4 32-bit subportions of
these 28 bytes. We specify the input by bit offsets from the
start of IP header or payload.
f1 = bits 32 to 63 of the IP header, comprising the IP
identification field, flags, and fragment offset.
f2 = bits 96 to 127 of the IP header, the source IP address.
f3 = bits 128 to 159 of the IP header, the destination IP
address.
f4 = bits 32 to 63 of the IP payload. For a TCP packet, f4
comprises the TCP sequence number followed by the message
length. For a UDP packet f4 comprises the UDP checksum.
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Hash Computation:
The hash is computed from f1, f2, f3 and f4 by a combination of
XOR (^), right shift (>>) and left shift (<<) operations. The
intermediate quantities h1, v1, v2 are 32-bit numbers.
1. v1 = f1 ^ f2;
2. v2 = f3 ^ f4;
3. h1 = v1 << 8;
4. h1 ^= v1 >> 4;
5. h1 ^= v1 >> 12;
6. h1 ^= v1 >> 16;
7. h1 ^= v2 << 6;
8. h1 ^= v2 << 10;
9. h1 ^= v2 << 14;
10. h1 ^= v2 >> 7;
The output of the hash is the least significant 16 bits of h1.
A.2 BOB Hash Function
The BOB Hash Function is a Hash Function designed for having
each bit of the input affecting every bit of the return value
and using both 1-bit and 2-bit deltas to achieve the so called
avalanche effect [Jenk97]. This function was originally built
for hash table lookup with fast software implementation.
Input Parameters:
The input parameters of such a function are:
- the length of the input string (key) to be hashed, in bytes.
The elementary input blocks of Bob hash are the single bytes,
therefore no padding is needed.
- an init value (an arbitrary 32-bit number).
Built in parameters:
The Bob Hash uses the following built-in parameter:
- the golden ratio (an arbitrary 32-bit number used in the hash
function computation: its purpose is to avoid mapping all zeros
to all zeros);
Note: the mix sub-function (see mix (a,b,c) macro in the
reference code in 3.2.4) has a number of parameters governing
the shifts in the registers. The one presented is not the only
possible choice.
It is an open point whether these may be considered additional
built-in parameters to specify at function configuration.
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Output.
The output of the BOB function is a 32-bit number. It should be
specified:
- A 32 bit mask to apply to the output
- The selection range as a list of non overlapping intervals
[start value, end value] where value is in [0,2^32]
Functioning:
The hash value is obtained computing first an initialization of
an internal state (composed of 3 32-bit numbers, called a, b, c
in the reference code below), then, for each input byte of the
key the internal state is combined by addition and mixed using
the mix sub-function. Finally, the internal state mixed one last
time and the third number of the state (c) is chosen as the
return value.
typedef unsigned long int ub4; /* unsigned 4-byte quantities
*/
typedef unsigned char ub1; /* unsigned 1-byte quantities
*/
#define hashsize(n) ((ub4)1<<(n))
#define hashmask(n) (hashsize(n)-1)
/* ------------------------------------------------------
mix -- mix 3 32-bit values reversibly.
For every delta with one or two bits set, and the deltas of
all three high bits or all three low bits, whether the original
value of a,b,c is almost all zero or is uniformly distributed,
* If mix() is run forward or backward, at least 32 bits in
a,b,c have at least 1/4 probability of changing.
* If mix() is run forward, every bit of c will change between
1/3 and 2/3 of the time. (Well, 22/100 and 78/100 for some 2-
bit deltas.) mix() was built out of 36 single-cycle latency
instructions in a structure that could supported 2x parallelism,
like so:
a -= b;
a -= c; x = (c>>13);
b -= c; a ^= x;
b -= a; x = (a<<8);
c -= a; b ^= x;
c -= b; x = (b>>13);
...
Unfortunately, superscalar Pentiums and Sparcs can't take
advantage of that parallelism. They've also turned some of
those single-cycle latency instructions into multi-cycle latency
instructions
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------------------------------------------------------------*/
#define mix(a,b,c) \
{ \
a -= b; a -= c; a ^= (c>>13); \
b -= c; b -= a; b ^= (a<<8); \
c -= a; c -= b; c ^= (b>>13); \
a -= b; a -= c; a ^= (c>>12); \
b -= c; b -= a; b ^= (a<<16); \
c -= a; c -= b; c ^= (b>>5); \
a -= b; a -= c; a ^= (c>>3); \
b -= c; b -= a; b ^= (a<<10); \
c -= a; c -= b; c ^= (b>>15); \
}
/* -----------------------------------------------------------
hash() -- hash a variable-length key into a 32-bit value
k : the key (the unaligned variable-length array of bytes)
len : the length of the key, counting by bytes
initval : can be any 4-byte value
Returns a 32-bit value. Every bit of the key affects every bit
of the return value. Every 1-bit and 2-bit delta achieves
avalanche. About 6*len+35 instructions.
The best hash table sizes are powers of 2. There is no need to
do mod a prime (mod is sooo slow!). If you need less than 32
bits, use a bitmask. For example, if you need only 10 bits, do
h = (h & hashmask(10));
In which case, the hash table should have hashsize(10) elements.
If you are hashing n strings (ub1 **)k, do it like this:
for (i=0, h=0; i<n; ++i) h = hash( k[i], len[i], h);
By Bob Jenkins, 1996. bob_jenkins@burtleburtle.net. You may
use this code any way you wish, private, educational, or
commercial. It's free. See
http://burtleburtle.net/bob/hash/evahash.html
Use for hash table lookup, or anything where one collision in
2^^32 is acceptable. Do NOT use for cryptographic purposes.
----------------------------------------------------------- */
ub4 bob_hash(k, length, initval)
register ub1 *k; /* the key */
register ub4 length; /* the length of the key */
register ub4 initval; /* an arbitrary value */
{
register ub4 a,b,c,len;
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/* Set up the internal state */
len = length;
a = b = 0x9e3779b9; /*the golden ratio; an arbitrary value
*/
c = initval; /* another arbitrary value */
/*------------------------------------ handle most of the key */
while (len >= 12)
{
a += (k[0] +((ub4)k[1]<<8) +((ub4)k[2]<<16)
+((ub4)k[3]<<24));
b += (k[4] +((ub4)k[5]<<8) +((ub4)k[6]<<16)
+((ub4)k[7]<<24));
c += (k[8] +((ub4)k[9]<<8)
+((ub4)k[10]<<16)+((ub4)k[11]<<24));
mix(a,b,c);
k += 12; len -= 12;
}
/*---------------------------- handle the last 11 bytes */
c += length;
switch(len) /* all the case statements fall through*/
{
case 11: c+=((ub4)k[10]<<24);
case 10: c+=((ub4)k[9]<<16);
case 9 : c+=((ub4)k[8]<<8);
/* the first byte of c is reserved for the length */
case 8 : b+=((ub4)k[7]<<24);
case 7 : b+=((ub4)k[6]<<16);
case 6 : b+=((ub4)k[5]<<8);
case 5 : b+=k[4];
case 4 : a+=((ub4)k[3]<<24);
case 3 : a+=((ub4)k[2]<<16);
case 2 : a+=((ub4)k[1]<<8);
case 1 : a+=k[0];
/* case 0: nothing left to add */
}
mix(a,b,c);
/*-------------------------------- report the result */
return c;
}
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