Indicators of Compromise (IoCs) and Their Role in Attack Defence
draft-ietf-opsec-indicators-of-compromise-04
The information below is for an old version of the document that is already published as an RFC.
| Document | Type |
This is an older version of an Internet-Draft that was ultimately published as RFC 9424.
|
|
|---|---|---|---|
| Authors | Kirsty Paine , Ollie Whitehouse , James Sellwood , Andrew S | ||
| Last updated | 2023-08-11 (Latest revision 2023-02-03) | ||
| Replaces | draft-paine-smart-indicators-of-compromise | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Intended RFC status | Informational | ||
| Formats | |||
| Reviews |
INTDIR Telechat review
(of
-03)
by Dave Thaler
Ready w/issues
|
||
| Additional resources | Mailing list discussion | ||
| Stream | WG state | Submitted to IESG for Publication | |
| Document shepherd | Jen Linkova | ||
| Shepherd write-up | Show Last changed 2022-11-09 | ||
| IESG | IESG state | Became RFC 9424 (Informational) | |
| Action Holders |
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|
||
| Consensus boilerplate | Yes | ||
| Telechat date | (None) | ||
| Responsible AD | Warren "Ace" Kumari | ||
| Send notices to | furry13@gmail.com | ||
| IANA | IANA review state | Version Changed - Review Needed | |
| IANA action state | No IANA Actions |
draft-ietf-opsec-indicators-of-compromise-04
OPSEC K. Paine
Internet-Draft Splunk Inc.
Intended status: Informational O. Whitehouse
Expires: 7 August 2023 Binary Firefly
J. Sellwood
A. Shaw
UK National Cyber Security Centre
3 February 2023
Indicators of Compromise (IoCs) and Their Role in Attack Defence
draft-ietf-opsec-indicators-of-compromise-04
Abstract
Cyber defenders frequently rely on Indicators of Compromise (IoCs) to
identify, trace, and block malicious activity in networks or on
endpoints. This draft reviews the fundamentals, opportunities,
operational limitations, and recommendations for IoC use. It
highlights the need for IoCs to be detectable in implementations of
Internet protocols, tools, and technologies - both for the IoCs'
initial discovery and their use in detection - and provides a
foundation for approaches to operational challenges in network
security.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 7 August 2023.
Copyright Notice
Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
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This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
extracted from this document must include Revised BSD License text as
described in Section 4.e of the Trust Legal Provisions and are
provided without warranty as described in the Revised BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. IoC Fundamentals . . . . . . . . . . . . . . . . . . . . . . 4
3.1. IoC Types and the Pyramid of Pain . . . . . . . . . . . . 4
3.2. IoC Lifecycle . . . . . . . . . . . . . . . . . . . . . . 8
3.2.1. Discovery . . . . . . . . . . . . . . . . . . . . . . 8
3.2.2. Assessment . . . . . . . . . . . . . . . . . . . . . 9
3.2.3. Sharing . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.4. Deployment . . . . . . . . . . . . . . . . . . . . . 10
3.2.5. Detection . . . . . . . . . . . . . . . . . . . . . . 10
3.2.6. Reaction . . . . . . . . . . . . . . . . . . . . . . 10
3.2.7. End of Life . . . . . . . . . . . . . . . . . . . . . 11
4. Using IoCs Effectively . . . . . . . . . . . . . . . . . . . 11
4.1. Opportunities . . . . . . . . . . . . . . . . . . . . . . 11
4.1.1. IoCs underpin and enable multiple layers of the modern
defence-in-depth strategy . . . . . . . . . . . . . . 11
4.1.2. IoCs can be used even with limited resources . . . . 12
4.1.3. IoCs have a multiplier effect on attack defence effort
within an organisation . . . . . . . . . . . . . . . 13
4.1.4. IoCs are easily shared between organisations . . . . 13
4.1.5. IoCs can provide significant time savings . . . . . . 14
4.1.6. IoCs allow for discovery of historic attacks . . . . 14
4.1.7. IoCs can be attributed to specific threats . . . . . 14
4.2. Case Studies . . . . . . . . . . . . . . . . . . . . . . 15
4.2.1. Cobalt Strike . . . . . . . . . . . . . . . . . . . . 15
4.2.1.1. Overall TTP . . . . . . . . . . . . . . . . . . . 15
4.2.1.2. IoCs . . . . . . . . . . . . . . . . . . . . . . 16
4.2.2. APT33 . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.2.1. Overall TTP . . . . . . . . . . . . . . . . . . . 16
4.2.2.2. IoCs . . . . . . . . . . . . . . . . . . . . . . 17
5. Operational Limitations . . . . . . . . . . . . . . . . . . . 18
5.1. Time and Effort . . . . . . . . . . . . . . . . . . . . . 18
5.1.1. Fragility . . . . . . . . . . . . . . . . . . . . . . 18
5.1.2. Discoverability . . . . . . . . . . . . . . . . . . . 19
5.1.3. Completeness . . . . . . . . . . . . . . . . . . . . 20
5.2. Precision . . . . . . . . . . . . . . . . . . . . . . . . 20
5.2.1. Specificity . . . . . . . . . . . . . . . . . . . . . 20
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5.2.2. Dual and Compromised Use . . . . . . . . . . . . . . 21
5.2.3. Changing Use . . . . . . . . . . . . . . . . . . . . 21
5.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.4. Automation . . . . . . . . . . . . . . . . . . . . . . . 22
6. Comprehensive Coverage and Defence-in-Depth . . . . . . . . . 23
7. Security Considerations . . . . . . . . . . . . . . . . . . . 26
8. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 26
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 26
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 26
11. Informative References . . . . . . . . . . . . . . . . . . . 26
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 29
1. Introduction
This draft describes the various types of Indicator of Compromise
(IoC) and how they are used effectively in attack defence (often
called cyber defence). It introduces concepts such as the Pyramid of
Pain [PoP] and the IoC lifecycle to highlight how IoCs may be used to
provide a broad range of defences. This draft provides suggestions
for implementers of controls based on IoCs, as well as potential
operational limitations. Two case studies which demonstrate the
usefulness of IoCs for detecting and defending against real world
attacks are included. One case study involves an intrusion set (a
set of malicious activity and behaviours attributed to one threat
actor) known as APT33 and the other an attack tool called Cobalt
Strike. This document is not a comprehensive report of APT33 or
Cobalt Strike and is intended to be read alongside publicly published
reports (referred to as open source material among cyber intelligence
practitioners) on these threats (for example, [Symantec] and
[NCCGroup], respectively).
2. Terminology
Attack defence: the activity of providing cyber security to an
environment through the prevention, detection and response to
attempted and successful cyber intrusions. A successful defence can
be achieved through the blocking, monitoring and response to
adversarial activity at a network, endpoint or application levels.
Command and control (C2) server: an attacker-controlled server used
to communicate with, send commands to and receive data from
compromised machines. Communication between a C2 server and
compromised hosts is called command and control traffic.
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Domain Generation Algorithm (DGA): used in malware strains to
periodically generate domain names (via algorithm). Malware may use
DGAs to compute a destination for C2 traffic, rather than relying on
a pre-assigned list of static IP addresses or domains that can be
blocked more easily when extracted from, or otherwise linked to, the
malware.
Kill chain: a model for conceptually breaking down a cyber intrusion
into stages of the attack from reconnaissance through to actioning
the attacker's objectives. This model allows defenders to think
about, discuss, plan for, and implement controls to defend discrete
phases of an attacker's activity [KillChain].
Tactics, Techniques, and Procedures (TTPs): the way an adversary
undertakes activities in the kill chain - the choices made, methods
followed, tools and infrastructure used, protocols employed, and
commands executed. If they are distinct enough, aspects of an
attacker's TTPs can form specific Indicators of Compromise (IoCs), as
if they were a fingerprint.
Control (as defined by US NIST): a safeguard or countermeasure
prescribed for an information system or an organisation designed to
protect the confidentiality, integrity, and availability of its
information and to meet a set of defined security requirements.
[NIST].
3. IoC Fundamentals
3.1. IoC Types and the Pyramid of Pain
Indicators of Compromise (IoCs) are observable artefacts relating to
an attacker or their activities, such as their tactics, techniques,
procedures, and associated tooling and infrastructure. These
indicators can be observed at network or endpoint (host) levels and
can, with varying degrees of confidence, help network defenders to
proactively block malicious traffic or code execution, determine a
cyber intrusion occurred, or associate discovered activity to a known
intrusion set and thereby potentially identify additional avenues for
investigation. IoCs are deployed to firewalls and other security
control points by adding them to the list of indicators that the
control point is searching for in the traffic that it is monitoring.
When associated with malicious activity, the following are some
examples of protocol-related IoCs:
* IPv4 and IPv6 addresses in network traffic.
* Fully qualified domain names (FQDNs) in network traffic, DNS
resolver caches or logs.
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* TLS Server Name Indication values in network traffic.
* Code signing certificates in binaries.
* TLS certificate information (such as SHA256 hashes) in network
traffic.
* Cryptographic hashes (e.g. MD5, SHA1 or SHA256) of malicious
binaries or scripts when calculated from network traffic or file
system artefacts.
* Attack tools (such as Mimikatz [Mimikatz]) and their code
structure and execution characteristics.
* Attack techniques, such as Kerberos golden tickets [GoldenTicket]
which can be observed in network traffic or system artefacts.
The common types of IoC form a 'Pyramid of Pain' [PoP] that informs
prevention, detection, and mitigation strategies. Each IoC type's
place in the pyramid represents how much 'pain' a typical adversary
experiences as part of changing the activity that produces that
artefact. The greater pain an adversary experiences (towards the
top) the less likely they are to change those aspects of their
activity and the longer the IoC is likely to reflect the attacker's
intrusion set - i.e., the less fragile those IoCs will be from a
defender's perspective. The layers of the PoP commonly range from
hashes up to TTPs, with the pain ranging from simply recompiling code
to creating a whole new attack strategy. Other types of IoC do exist
and could be included in an extended version of the PoP should that
assist the defender to understand and discuss intrusion sets most
relevant to them.
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/\
/ \ MORE PAIN
/ \ LESS FRAGILE
/ \ LESS PRECISE
/ TTPs \
/ \ / \
============== |
/ \ |
/ Tools \ |
/ \ |
====================== |
/ \ |
/ Network/Host Artefacts \ |
/ \ |
============================== |
/ \ |
/ Domain Names \ |
/ \ |
====================================== |
/ \ |
/ IP Addresses \ |
/ \ \ /
==============================================
/ \ LESS PAIN
/ Hash Values \ MORE FRAGILE
/ \ MORE PRECISE
======================================================
Figure 1
On the lowest (and least painful) level are hashes of malicious
files. These are easy for a defender to gather and can be deployed
to firewalls or endpoint protection to block malicious downloads or
prevent code execution. While IoCs aren't the only way for defenders
to do this kind of blocking, they are a quick, convenient, and
unintrusive method. Hashes are precise detections for individual
files based on their binary content. To subvert this defence,
however, an adversary need only recompile code, or otherwise modify
the file content with some trivial changes, to modify the hash value.
The next two levels are IP addresses and domain names. Interactions
with these may be blocked, with varying false positive rates
(misidentifying non-malicious traffic as malicious, see Section 5),
and often cause more pain to an adversary to subvert than file
hashes. The adversary may have to change IP ranges, find a new
provider, and change their code (e.g., if the IP address is hard-
coded, rather than resolved). A similar situation applies to domain
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names, but in some cases threat actors have specifically registered
these to masquerade as a particular organisation or to otherwise
falsely imply or claim an association that will be convincing or
misleading to those they are attacking. While the process and cost
of registering new domain names are now unlikely to be prohibitive or
distracting to many attackers, there is slightly greater pain in
selecting unregistered, but appropriate, domain names for such
purposes.
Network and endpoint artefacts, such as a malware's beaconing pattern
on the network or the modified timestamps of files touched on an
endpoint, are harder still to change as they relate specifically to
the attack taking place and, in some cases, may not be under the
direct control of the attacker. However, more sophisticated
attackers use TTPs or tooling that provide flexibility at this level
(such as Cobalt Strike's malleable command and control [COBALT]) or a
means by which some artefacts can be masked (see [Timestomp]).
Tools and TTPs form the top two levels of the pyramid; these levels
describe a threat actor's methodology - the way they perform the
attack. The tools level refers specifically to the software (and
less frequently hardware) used to conduct the attack, whereas the
TTPs level picks up on all the other aspects of the attack strategy.
IoCs at these levels are more complicated and complex - for example
they can include the details of how an attacker deploys malicious
code to perform reconnaissance of a victim's network, that pivots
laterally to a valuable endpoint, and then downloads a ransomware
payload. TTPs and tools take intensive effort to diagnose on the
part of the defender, but they are fundamental to the attacker and
campaign and hence incredibly painful for the adversary to change.
The variation in discoverability of IoCs is indicated by the numbers
of IoCs in the open threat intelligence community Alienvault
[ALIENVAULT]. As of January 2023, Alienvault contained:
* Groups (i.e., combinations of TTPs): 631
* Malware families (i.e., tools): ~27,000
* URL: 2,854,918
* Domain names: 64,769,363
* IPv4 addresses: 5,427,762
* IPv6 addresses: 12,009
* SHA256 hash values: 5,452,442
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The number of domain names appears out of sync with the other counts,
which reduce on the way up the PoP. This discrepancy warrants
further research; however, contributing factors may be the use of
DGAs and the fact that threat actors use domain names to masquerade
as legitimate organisations and so have added incentive for creating
new domain names as they are identified and confiscated.
3.2. IoC Lifecycle
To be of use to defenders, IoCs must first be discovered, assessed,
shared, and deployed. When a logged activity is identified and
correlated to an IoC this detection triggers a reaction by the
defender which may include an investigation, potentially leading to
more IoCs being discovered, assessed, shared, and deployed. This
cycle continues until such time that the IoC is determined to no
longer be relevant, at which point it is removed from the control
space.
3.2.1. Discovery
IoCs are discovered initially through manual investigation or
automated analysis. They can be discovered in a range of sources,
including at endpoints and in the network (on the wire). They must
either be extracted from logs monitoring protocol packet captures,
code execution or system activity (in the case of hashes, IP
addresses, domain names, and network or endpoint artefacts), or be
determined through analysis of attack activity or tooling. In some
cases, discovery may be a reactive process, where IoCs from past or
current attacks are identified from the traces left behind. However,
discovery may also result from proactive hunting for potential future
IoCs extrapolated from knowledge of past events (such as from
identifying attacker infrastructure by monitoring domain name
registration patterns).
Crucially, for an IoC to be discovered, the indicator must be
extractable from the internet protocol, tool, or technology it is
associated with. Identifying a particular exchange (or sequence of
exchanged messages) related to an attack is of limited benefit if
indicators cannot be extracted, or, once they are extracted, cannot
be subsequently associated with a later related exchange of messages
or artefacts in the same, or in a different, protocol. If it is not
possible to tell the source or destination of malicious attack
traffic, it will not be possible to identify and block subsequent
attack traffic either.
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3.2.2. Assessment
Defenders may treat different IoCs differently, depending on the
IoCs' quality and the defender's needs and capabilities. Defenders
may, for example, place differing trust in IoCs depending on their
source, freshness, confidence level, or the associated threat. These
decisions rely on associated contextual information recovered at the
point of discovery or provided when the IoC was shared.
An IoC without context is not much use for network defence. On the
other hand, an IoC delivered with context (for example the threat
actor it relates to, its role in an attack, the last time it was seen
in use, its expected lifetime, or other related IoCs) allows a
network defender to make an informed choice on how to use it to
protect their network - for example, whether to simply log it,
actively monitor it, or out-right block it.
3.2.3. Sharing
Once discovered and assessed, IoCs are most helpful when deployed in
such a way to have a broad impact on the detection or disruption of
threats, or shared at scale so many individuals and organisations can
defend themselves. An IoC may be shared individually (with
appropriate context) in an unstructured manner or may be packaged
alongside many other IoCs in a standardised format, such as
Structured Threat Information Expression [STIX], MISP Core
[MISPCORE], OpenIOC [OPENIOC] and IODEF [RFC7970]. This enables
distribution via a structured feed, such as one implementing Trusted
Automated Exchange of Intelligence Information [TAXII], or through a
Malware Information Sharing Platform [MISP].
While some security companies and some membership-based groups (
often dubbed Information Sharing and Analysis Centres (ISACs) or
Information Sharing and Analysis Organizations (ISAOs)) provide paid
intelligence feeds containing IoCs, there are various free IoC
sources available from individual security researchers up through
small trust groups to national governmental cyber security
organisations and international Computer Emergency Response Teams
(CERTs). Whomever they are, sharers commonly indicate the extent to
which receivers may further distribute IoCs using frameworks like the
Traffic Light Protocol [TLP]. At its simplest, this indicates that
the receiver may share with anyone (TLP:CLEAR), share within the
defined sharing community (TLP: GREEN), share within their
organisation and their clients (TLP:AMBER+STRICT), share just within
their organisation (TLP:AMBER), or not share with anyone outside the
original specific IoC exchange (TLP:RED).
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3.2.4. Deployment
For IoCs to provide defence-in-depth (see Section 6) and so cope with
different points of failure, correct deployment is important.
Different IoCs will detect malicious activity at different layers of
the network stack and at different stages of an attack, so deploying
a range of IoCs enables layers of defence at each security control,
reinforcing the benefits of using multiple security controls as part
of a defence-in-depth solution. The network security controls and
endpoint solutions where they are deployed need to have sufficient
privilege, and sufficient visibility, to detect IoCs and to act on
them. Wherever IoCs exist they need to be made available to security
controls and associated apparatus to ensure they can be deployed
quickly and widely. While IoCs may be manually assessed after
discovery or receipt, significant advantage may be gained by
automatically ingesting, processing, assessing, and deploying IoCs
from logs or intelligence feeds to the appropriate security controls.
As not all IoCs are of the same quality, confidence in IoCs drawn
from each threat intelligence feed should be considered when deciding
whether to deploy IoCs automatically in this way.
IoCs can be particularly effective at mitigating malicious activity
when deployed in security controls with the broadest impact. This
could be achieved by developers of security products or firewalls
adding support for the distribution and consumption of IoCs directly
to their products, without each user having to do it - thus
addressing the threat for the whole user base at once in a machine
scalable and automated manner. This could also be acheived within an
enterprise by ensuring those control points with the widest aperture,
for example enterprise-wide DNS resolvers, are able to act
automatically based on IoC feeds.
3.2.5. Detection
Security controls with deployed IoCs monitor their relevant control
space and trigger a generic or specific reaction upon detection of
the IoC in monitored logs or on network interfaces.
3.2.6. Reaction
The reaction to an IoC's detection may differ depending on factors
such as the capabilities and configuration of the control it is
deployed in, the assessment of the IoC, and the properties of the log
source in which it was detected. For example, a connection to a
known botnet C2 server may indicate a problem but does not guarantee
it, particularly if the server is a compromised host still performing
some other legitimate functions. Common reactions include event
logging, triggering alerts, and blocking or terminating the source of
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the activity.
3.2.7. End of Life
How long an IoC remains useful varies and is dependent on factors
including initial confidence level, fragility, and precision of the
IoC (discussed further in Section 5). In some cases, IoCs may be
automatically 'aged' based on their initial characteristics and so
will reach end of life at a predetermined time. In other cases, IoCs
may become invalidated due to a shift in the threat actor's TTPs
(e.g., resulting from a new development or their discovery) or due to
remediation action taken by a defender. End of life may also come
about due to an activity unrelated to attack or defence, such as when
a third-party service used by the attacker changes or goes offline.
Whatever the cause, IoCs should be removed from detection at the end
of their life to reduce the likelihood of false positives.
4. Using IoCs Effectively
4.1. Opportunities
IoCs offer a variety of opportunities to cyber defenders as part of a
modern defence-in-depth strategy. No matter the size of an
organisation, IoCs can provide an effective, scalable, and efficient
defence mechanism against classes of attack from the latest threats
or specific intrusion sets which may have struck in the past.
4.1.1. IoCs underpin and enable multiple layers of the modern defence-
in-depth strategy
Firewalls, Intrusion Detection Systems (IDS), and Intrusion
Prevention Systems (IPS) all employ IoCs to identify and mitigate
threats across networks. Anti-Virus (AV) and Endpoint Detection and
Response (EDR) products deploy IoCs via catalogues or libraries to
supported client endpoints. Security Incident Event Management
(SIEM) platforms compare IoCs against aggregated logs from various
sources - network, endpoint, and application. Of course, IoCs do not
address all attack defence challenges - but they form a vital tier of
any organisation's layered defence. Some types of IoC may be present
across all those controls while others may be deployed only in
certain layers of a defence-in-depth solution. Further, IoCs
relevant to a specific kill chain may only reflect activity performed
during a certain phase and so need to be combined with other IoCs or
mechanisms for complete coverage of the kill chain as part of an
intrusion set.
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As an example, open source malware can be deployed by many different
actors, each using their own TTPs and infrastructure. However, if
the actors use the same executable, the hash remains the same and
this IoC can be deployed in endpoint protection to block execution
regardless of individual actor, infrastructure, or other TTPs.
Should this defence fail in a specific case, for example if an actor
recompiles the executable binary producing a unique hash, other
defences can prevent them progressing further through their attack -
for instance, by blocking known malicious domain name look-ups and
thereby preventing the malware calling out to its C2 infrastructure.
Alternatively, another malicious actor may regularly change their
tools and infrastructure (and thus the indicators associated with the
intrusion set) deployed across different campaigns, but their access
vectors may remain consistent and well-known. In this case, this
access TTP can be recognised and proactively defended against even
while there is uncertainty of the intended subsequent activity. For
example, if their access vector consistently exploits a vulnerability
in software, regular and estate-wide patching can prevent the attack
from taking place. Should these pre-emptive measures fail however,
other IoCs observed across multiple campaigns may be able to prevent
the attack at later stages in the kill chain.
4.1.2. IoCs can be used even with limited resources
IoCs are inexpensive, scalable, and easy to deploy, making their use
particularly beneficial for smaller entities, especially where they
are exposed to a significant threat. For example, a small
manufacturing subcontractor in a supply chain producing a critical,
highly specialised component may represent an attractive target
because there would be disproportionate impact on both the supply
chain and the prime contractor if it were compromised. It may be
reasonable to assume that this small manufacturer will have only
basic security (whether internal or outsourced) and while it is
likely to have comparatively fewer resources to manage the risks it
faces compared to larger partners, it can still leverage IoCs to
great effect. Small entities like this can deploy IoCs to give a
baseline protection against known threats without having access to a
well-resourced, mature defensive team and the threat intelligence
relationships necessary to perform resource-intensive investigations.
While some level of expertise on the part of such a small company
would be needed to successfully deploy IoCs, use of IoCs does not
require the same intensive training as needed for more subjective
controls, such as those using machine learning which require further
manual analysis of identified events to verify if they are indeed
malicious. In this way, a major part of the appeal of IoCs is that
they can afford some level of protection to organisations across
spectrums of resource capability, maturity, and sophistication.
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4.1.3. IoCs have a multiplier effect on attack defence effort within an
organisation
Individual IoCs can provide widespread protection that scales
effectively for defenders across an organisation or ecosystem.
Within a single organisation, simply blocking one IoC may protect
thousands of users and that blocking may be performed (depending on
the IoC type) across multiple security controls monitoring numerous
different types of activity within networks, endpoints, and
applications. The prime contractor from our earlier example can
supply IoCs to the small subcontractor and so further uplift that
smaller entity's defensive capability and at the same time protect
itself and its interests.
Multiple organisations may benefit through directly receiving shared
IoCs (see Section 4.1.4), but they may also benefit through the IoCs'
application in services they utilise. In the case of an ongoing
email phishing campaign, IoCs can be monitored, discovered, and
deployed quickly and easily by individual organisations. However, if
they are deployed quickly via a mechanism such as a protective DNS
filtering service, they can be more effective still - an email
campaign may be mitigated before some organisations' recipients ever
click the link or before some malicious payloads can call out for
instructions. Through such approaches other parties can be protected
without direct sharing of IoCs with those organisation, or additional
effort.
4.1.4. IoCs are easily shared between organisations
IoCs can also be very easily shared between individuals and
organisations. Firstly, IoCs are easy to distribute as they can be
represented concisely as text (possibly in hexadecimal) and so are
frequently exchanged in small numbers in emails, blog posts, or
technical reports. Secondly, standards, such as those mentioned in
Section 3.2.3, exist to provide well-defined formats for sharing
large collections or regular sets of IoC along with all the
associated context. While discovering one IoC can be intensive, once
shared via well-established routes (as discussed in Section 3.2.2)
that individual IoC may, further, protect thousands of organisations
and so all of their users. Quick and easy sharing of IoCs gives
blanket coverage for organisations and allows widespread mitigation
in a timely fashion - they can be shared with systems administrators,
from small to large organisations and from large teams to single
individuals, allowing them all to implement defences on their
networks.
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4.1.5. IoCs can provide significant time savings
Not only are there time savings from sharing IoCs, saving duplication
of investigation effort, but deploying them automatically at scale is
seamless for many enterprises. Where automatic deployment of IoCs is
working well, organisations and users get blanket protection with
minimal human intervention and minimal effort, a key goal of attack
defence. The ability to do this at scale and at pace is often vital
when responding to agile threat actors that may change their
intrusion set frequently and so the relevant IoCs also change.
Conversely, protecting a complex network without automatic deployment
of IoCs could mean manually updating every single endpoint or network
device consistently and reliably to the same security state. The
work this entails (including locating assets and devices, polling for
logs and system information, and manually checking patch levels)
introduces complexity and a need for skilled analysts and engineers.
While it is still necessary to invest effort both to enable efficient
IoC deployment, and to eliminate false positives when widely
deploying IoCs, the cost and effort involved can be far smaller than
the work entailed in reliably manually updating all endpoint and
network devices. For example, particularly on legacy systems that
may be particularly complicated, or even impossible, to update.
4.1.6. IoCs allow for discovery of historic attacks
A network defender can use recently acquired IoCs in conjunction with
historic data, such as logged DNS queries or email attachment hashes,
to hunt for signs of past compromise. Not only can this technique
help to build up a clear picture of past attacks, but it also allows
for retrospective mitigation of the effects of any previous
intrusion. This opportunity is reliant on historic data not having
been compromised itself, by a technique such as Timestomp
[Timestomp], and not being incomplete due to data retention policies,
but is nonetheless valuable for detecting and remediating past
attacks.
4.1.7. IoCs can be attributed to specific threats
Deployment of various modern security controls, such as firewall
filtering or EDR, come with an inherent trade-off between breadth of
protection and various costs, including the risk of false positives
(see Section 5.2 ), staff time, and pure financial costs.
Organisations can use threat modelling and information assurance to
assess and prioritise risk from identified threats and to determine
how they will mitigate or accept each of them. Contextual
information tying IoCs to specific threats or actors and shared
alongside the IoCs enables organisations to focus their defences
against particular risks. This contextual information is generally
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expected by those receiving IoCs as it allows them the technical
freedom and capability to choose their risk appetite, security
posture and defence methods. The ease of sharing this contextual
information alongside IoCs, in part due to the formats outlined in
Section 3.2.3, makes it easier to track malicious actors across
campaigns and targets. Producing this contextual information before
sharing IoCs can take intensive analytical effort as well as
specialist tools and training. At its simplest it can involve
documenting sets of IoCs from multiple instances of the same attack
campaign, say from multiple unique payloads (and therefore with
distinct file hashes) from the same source and connecting to the same
C2 server. A more complicated approach is to cluster similar
combinations of TTPs seen across multiple campaigns over a period of
time. This can be used alongside detailed malware reverse
engineering and target profiling, overlaid on a geopolitical and
criminal backdrop, to infer attribution to a single threat actor.
4.2. Case Studies
The following two case studies illustrate how IoCs may be identified
in relation to threat actor tooling (in the first) and a threat actor
campaign (in the second). The case studies further highlight how
these IoCs may be used by cyber defenders.
4.2.1. Cobalt Strike
Cobalt Strike [COBALT] is a commercial attack framework used for
penetration testing that consists of an implant framework (beacon),
network protocol, and a C2 server. The beacon and network protocol
are highly malleable, meaning the protocol representation 'on the
wire' can be easily changed by an attacker to blend in with
legitimate traffic by ensuring the traffic conforms to the protocol
specification e.g. HTTP. The proprietary beacon supports TLS
encryption overlaid with a custom encryption scheme based on a
public-private keypair. The product also supports other techniques,
such as domain fronting [DFRONT], in attempt to avoid obvious passive
detection by static network signatures of domain names or IP
addresses. Domain fronting is used to blend traffic to a malicious
domain in with traffic originating from a network to an already
regularly communicated with domain over HTTPS.
4.2.1.1. Overall TTP
A beacon configuration describes how the implant should operate and
communicate with its C2 server. This configuration also provides
ancillary information such as the Cobalt Strike user's licence
watermark.
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4.2.1.2. IoCs
Tradecraft has been developed that allows the fingerprinting of C2
servers based on their responses to specific requests. This allows
the servers to be identified and then their beacon configurations to
be downloaded and the associated infrastructure addresses extracted
as IoCs.
The resulting mass IoCs for Cobalt Strike are:
* IP addresses of the C2 servers
* domain names used
Whilst these IoCs need to be refreshed regularly (due to the ease of
which they can be changed), the authors' experience of protecting
public sector organisations show these IoCs are effective for
disrupting threat actor operations that use Cobalt Strike.
These IoCs can be used to check historical data for evidence of past
compromise, as well as deployed to detect or block future infection
in a timely manner, thereby contributing to preventing the loss of
user and system data.
4.2.2. APT33
In contrast to the first case study, this describes a current
campaign by the threat actor APT33, also known as Elfin and Refined
Kitten (see [Symantec]). APT33 has been assessed by industry to be a
state-sponsored group [FireEye2], yet in this case study, IoCs still
gave defenders an effective tool against such a powerful adversary.
The group has been active since at least 2015 and is known to target
a range of sectors including petrochemical, government, engineering,
and manufacturing. Activity has been seen in countries across the
globe, but predominantly in the USA and Saudi Arabia.
4.2.2.1. Overall TTP
The techniques employed by this actor exhibit a relatively low level
of sophistication considering it is a state-sponsored group;
typically, APT33 performs spear phishing (sending targeted malicious
emails to a limited number of pre-selected recipients) with document
lures that imitate legitimate publications. User interaction with
these lures executes the initial payload and enables APT33 to gain
initial access. Once inside a target network, APT33 attempts to
pivot to other machines to gather documents and gain access to
administrative credentials. In some cases, users are tricked into
providing credentials that are then used with RULER [RULER], a freely
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available tool that allows exploitation of an email client. The
attacker, in possession of a target's password, uses RULER to access
the target's mail account and embeds a malicious script which will be
triggered when the mail client is next opened, resulting in the
execution of malicious code (often additional malware retrieved from
the Internet) (see [FireEye]).
APT33 sometimes deploys a destructive tool which overwrites the
master boot record (MBR) of the hard drives in as many PCs as
possible. This type of tool, known as a wiper, results in data loss
and renders devices unusable until the operating system is
reinstalled. In some cases, the actor uses administrator credentials
to invoke execution across a large swathe of a company's IT estate at
once; where this isn't possible the actor may attempt to spread the
wiper first manually or by using worm-like capabilities against
unpatched vulnerabilities on the networked computers.
4.2.2.2. IoCs
As a result of investigations by a partnership of industry and the
UK's National Cyber Security Centre (NCSC), a set of IoCs were
compiled and shared with both public and private sector organisations
so network defenders could search for them in their networks.
Detection of these IoCs is likely indicative of APT33 targeting and
could indicate potential compromise and subsequent use of destructive
malware. Network defenders could also initiate processes to block
these IoCs to foil future attacks. This set of IoCs comprised:
* 9 hashes and email subject lines
* 5 IP addresses
* 7 domain names
In November 2021, a joint advisory concerning APT33 [CISA] was issued
by Federal Bureau of Investigation (FBI), the Cybersecurity and
Infrastructure Security Agency (CISA), the Australian Cyber Security
Centre (ACSC), and NCSC. This outlined recent exploitation of
vulnerabilities by APT33, providing a thorough overview of observed
TTPs, as well as sharing further IoCs:
* 8 hashes of malicious executables
* 3 IP addresses
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5. Operational Limitations
The different IoC types inherently embody a set of trade-offs for
defenders between the risk of false positives (misidentifying non-
malicious traffic as malicious) and the risk of failing to identify
attacks. The attacker's relative pain of modifying attacks to
subvert known IoCs, as discussed using the Pyramid of Pain (PoP) in
Section 3.1, inversely correlates with the fragility of the IoC and
with the precision with which the IoC identifies an attack. Research
is needed to elucidate the exact nature of these trade-offs between
pain, fragility, and precision.
5.1. Time and Effort
5.1.1. Fragility
As alluded to in Section 3.1, the Pyramid of Pain can be thought of
in terms of fragility for the defender as well as pain for the
attacker. The less painful it is for the attacker to change an IoC,
the more fragile that IoC is as a defence tool. It is relatively
simple to determine the hash value for various malicious file
attachments observed as lures in a phishing campaign and to deploy
these through AV or an email gateway security control. However,
those hashes are fragile and can (and often will) be changed between
campaigns. Malicious IP addresses and domain names can also be
changed between campaigns, but this may happen less frequently due to
the greater pain of managing infrastructure compared to altering
files, and so IP addresses and domain names may provide a less
fragile detection capability.
This does not mean the more fragile IoC types are worthless.
Firstly, there is no guarantee a fragile IoC will change, and if a
known IoC isn't changed by the attacker but wasn't blocked then the
defender missed an opportunity to halt an attack in its tracks.
Secondly, even within one IoC type, there is variation in the
fragility depending on the context of the IoC. The file hash of a
phishing lure document (with a particular theme and containing a
specific staging server link) may be more fragile than the file hash
of a remote access trojan payload the attacker uses after initial
access. That in turn may be more fragile than the file hash of an
attacker-controlled post-exploitation reconnaissance tool that
doesn't connect directly to the attacker's infrastructure. Thirdly,
some threats and actors are more capable or inclined to change than
others, and so the fragility of an IoC for one may be very different
to an IoC of the same type for another actor.
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Ultimately, fragility is a defender's concern that impacts the
ongoing efficacy of each IoC and will factor into decisions about end
of life. However, it should not prevent adoption of individual IoCs
unless there are significantly strict resource constraints that
demand down-selection of IoCs for deployment. More usually,
defenders researching threats will attempt to identify IoCs of
varying fragilities for a particular kill chain to provide the
greatest chances of ongoing detection given available investigative
effort (see Section 5.1.2) and while still maintaining precision (see
Section 5.2).
5.1.2. Discoverability
To be used in attack defence, IoCs must first be discovered through
proactive hunting or reactive investigation. As noted in
Section 3.1, IoCs in the tools and TTPs levels of the PoP require
intensive effort and research to discover. However, it is not just
an IoC's type that impacts its discoverability. The sophistication
of the actor, their TTPs, and their tooling play a significant role,
as does whether the IoC is retrieved from logs after the attack or
extracted from samples or infected systems earlier.
For example, on an infected endpoint it may be possible to identify a
malicious payload and then extract relevant IoCs, such as the file
hash and its C2 server address. If the attacker used the same static
payload throughout the attack this single file hash value will cover
all instances. If, however, the attacker diversified their payloads,
that hash can be more fragile and other hashes may need to be
discovered from other samples used on other infected endpoints.
Concurrently, the attacker may have simply hard-coded configuration
data into the payload, in which case the C2 server address can be
easy to recover. Alternatively, the address can be stored in an
obfuscated persistent configuration either within the payload (e.g.,
within its source code or associated resource) or the infected
endpoint's filesystem (e.g., using alternative data streams [ADS])
and thus requiring more effort to discover. Further, the attacker
may be storing the configuration in memory only or relying on a
domain generation algorithm (DGA) to generate C2 server addresses on
demand. In this case, extracting the C2 server address can require a
memory dump or the execution or reverse engineering of the DGA, all
of which increase the effort still further.
If the malicious payload has already communicated with its C2 server,
then it may be possible to discover that C2 server address IoC from
network traffic logs more easily. However, once again multiple
factors can make discoverability more challenging, such as the
increasing adoption of HTTPS for malicious traffic - meaning C2
communications blend in with legitimate traffic, and can be
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complicated to identify. Further, some malwares obfuscate their
intended destinations by using alternative DNS resolution services
(e.g., OpenNIC [OPENNIC]), encrypted DNS protocols such as DNS-over-
HTTPS [OILRIG], or by performing transformation operations on
resolved IP addresses to determine the real C2 server address encoded
in the DNS response [LAZARUS].
5.1.3. Completeness
In many cases the list of indicators resulting from an activity or
discovered in a malware sample is relatively short and so only adds
to the total set of all indicators in a limited and finite manner. A
clear example of this is when static indicators for C2 servers are
discovered in a malware strain. Sharing, deployment, and detection
will often not be greatly impacted by the addition of such indicators
for one more incident or one more sample. However, in the case of
discovery of a domain generation algorithm (DGA) this requires a
reimplementation of the algorithm and then execution to generate a
possible list of domains. Depending on the algorithm, this can
result in very large lists of indicators which may cause performance
degradation, particularly during detection. In some cases, such
sources of indicators can lead to a pragmatic decision being taken
between obtaining reasonable coverage of the possible indicator
values and theoretical completeness of a list of all possible
indicator values.
5.2. Precision
5.2.1. Specificity
Alongside pain and fragility, the PoP's levels can also be considered
in terms of how precise the defence can be, with the false positive
rate usually increasing as we move up the pyramid to less specific
IoCs. A hash value identifies a particular file, such as an
executable binary, and given a suitable cryptographic hash function
the false positives are effectively nil; by suitable we mean one with
preimage resistance and strong collision resistance. In comparison,
IoCs in the upper levels (such as some network artefacts or tool
fingerprints) may apply to various malicious binaries, and even
benign software may share the same identifying characteristics. For
example, threat actor tools making web requests may be identified by
the user-agent string specified in the request header. However, this
value may be the same as used by legitimate software, either by the
attacker's choice or through use of a common library.
It should come as no surprise that the more specific an IoC the more
fragile it is - as things change, they move outside of that specific
focus. While less fragile IoCs may be desirable for their robustness
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and longevity, this must be balanced with the increased chance of
false positives from their broadness. One way in which this balance
is achieved is by grouping indicators and using them in combination.
While two low-specificity IoCs for a particular attack may each have
chances of false positives, when observed together they may provide
greater confidence of an accurate detection of the relevant kill
chain.
5.2.2. Dual and Compromised Use
As noted in Section 3.2.2, the context of an IoC, such as the way in
which the attacker uses it, may equally impact the precision with
which that IoC detects an attack. An IP address representing an
attacker's staging server, from which their attack chain downloads
subsequent payloads, offers a precise IP address for attacker-owned
infrastructure. However, it will be less precise if that IP address
is associated with a cloud hosting provider and it is regularly
reassigned from one user to another; and it will be less precise
still if the attacker compromised a legitimate web server and is
abusing the IP address alongside the ongoing legitimate use.
Similarly, a file hash representing an attacker's custom remote
access trojan will be very precise; however, a file hash representing
a common enterprise remote administration tool will be less precise
depending on whether the defender organisation usually uses that tool
for legitimate systems administration or not. Notably, such dual use
indicators are context specific both in whether they are usually used
legitimately and in the way they are used in a particular
circumstance. Use of the remote administration tool may be
legitimate for support staff during working hours, but not generally
by non-support staff, particularly if observed outside of that
employee's usual working hours.
It is reasons such as these that context is so important when sharing
and using IoCs.
5.2.3. Changing Use
In the case of IP addresses, the growing adoption of cloud services,
proxies, virtual private networks (VPNs), and carrier grade network
address translation (NAT) are ever-increasing the number of systems
associated with any one IP address at the same moment in time. This
ongoing change to the use of IP addresses is somewhat reducing the
specificity of IP addresses (at least for specific subnets or
individual addresses) while also 'side- stepping' the pain that
threat actors would otherwise incur if they needed to change IP
address.
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5.3. Privacy
As noted in Section 3.2.2, context is critical to effective detection
using IoCs. However, at times, defenders may feel there are privacy
concerns with how much to share about a cyber intrusion, and with
whom. For example, defenders may generalise the IoCs' description of
the attack, by removing context to facilitate sharing. This
generalisation can result in an incomplete set of IoCs being shared
or IoCs being shared without clear indication of what they represent
and how they are involved in an attack. The sharer will consider the
privacy trade-off when generalising the IoC, and should bear in mind
that the loss of context can greatly reduce the utility of the IoC
for those they share with.
In the authors' experiences, self-censoring by sharers appears more
prevalent and more extensive when sharing IoCs into groups with more
members, into groups with a broader range of perceived member
expertise (particularly, the further the lower bound extends below
the sharer's perceived own expertise), and into groups that do not
maintain strong intermember trust. Trust within such groups often
appears strongest where members: interact regularly; have common
backgrounds, expertise, or challenges; conform to behavioural
expectations (such as by following defined handling requirements and
not misrepresenting material they share); and reciprocate the sharing
and support they receive. [LITREVIEW] highlights many of these
factors are associated with the human role in Cyber Threat
Intelligence (CTI) sharing.
5.4. Automation
While IoCs can be effectively utilised by organisations of various
sizes and resource constraints, as discussed in Section 4.1.2,
automation of IoC ingestion, processing, assessment, and deployment
is critical for managing them at scale. Manual oversight and
investigation may be necessary intermittently, but a reliance on
manual processing and searching only works at small scale or for
occasional cases.
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The adoption of automation can also enable faster and easier
correlation of IoC detections across different log sources and
network monitoring interfaces, across different times and physical
locations. Thereby, the response can be tailored to reflect the
number and overlap of detections from a particular intrusion set, and
the necessary context can be presented alongside the detection when
generating any alerts for defender review. While manual processing
and searching may be no less accurate (although IoC transcription
errors are a common problem during busy incidents in the experience
of the authors), the correlation and cross-referencing necessary to
provide the same degree of situational awareness is much more time-
consuming.
A third important consideration when performing manual processing is
the longer phase monitoring and adjustment necessary to effectively
age out IoCs as they become irrelevant or, more crucially,
inaccurate. Manual implementations must often simply include or
exclude an IoC, as anything more granular is time-consuming and
complicated to manage. In contrast, automations can support a
gradual reduction in confidence scoring enabling IoCs to contribute
but not individually disrupt a detection as their specificity
reduces.
6. Comprehensive Coverage and Defence-in-Depth
IoCs provide the defender with a range of options across the Pyramid
of Pain's (PoP) layers, enabling them to balance precision and
fragility to give high confidence detections that are practical and
useful. Broad coverage of the PoP is important as it allows the
defender to choose between high precision but high fragility options
and more robust but less precise indicators depending on
availability. As fragile indicators are changed, the more robust
IoCs allow for continued detection and faster rediscovery. For this
reason, it's important to collect as many IoCs as possible across the
whole PoP to provide options for defenders.
At the top of the PoP, TTPs identified through anomaly detection and
machine learning are more likely to have false positives, which gives
lower confidence and, vitally, requires better trained analysts to
understand and implement the defences. However, these are very
painful for attackers to change and so when tuned appropriately
provide a robust detection. Hashes, at the bottom, are precise and
easy to deploy but are fragile and easily changed within and across
campaigns by malicious actors.
Endpoint Detection and Response (EDR) or Anti-Virus (AV) are often
the first port of call for protection from intrusion, but endpoint
solutions aren't a panacea. One issue is that there are many
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environments where it is not possible to keep them updated, or in
some cases, deploy them at all. For example, the Owari botnet, a
Mirai variant [Owari], exploited Internet of Things (IoT) devices
where such solutions could not be deployed. It is because of such
gaps, where endpoint solutions can't be relied on, that a defence-in-
depth approach is commonly advocated, using a blended approach that
includes both network and endpoint defences.
If an attack happens, then the best situation is that an endpoint
solution will detect and prevent it. If it doesn't, it could be for
many good reasons: the endpoint solution could be quite conservative
and aim for a low false-positive rate; it might not have ubiquitous
coverage; or it might only be able to defend the initial step of the
kill chain [KillChain]. In the worst cases, the attack specifically
disables the endpoint solution or the malware is brand new and so
won't be recognised.
In the middle of the pyramid, IoCs related to network information
(such as domains and IP addresses) can be particularly useful. They
allow for broad coverage, without requiring each and every endpoint
security solution to be updated, as they may be detected and enforced
in a more centralised manner at network choke points (such as proxies
and gateways). This makes them particular useful in contexts where
ensuring endpoint security isn't possible such as "Bring Your Own
Device" (BYOD), Internet of Things (IoT) and legacy environments.
It's important to note that these network-level IoCs can also protect
users of a network against compromised endpoints when these IoCs are
used to detect the attack in network traffic, even if the compromise
itself passes unnoticed. For example, in a BYOD environment,
enforcing security policies on the device can be difficult, so non-
endpoint IoCs and solutions are needed to allow detection of
compromise even with no endpoint coverage.
One example of how network-level IoCs provide a layer of a defence-
in-depth solution is Protective DNS (PDNS) [Annual2021], a free and
voluntary DNS filtering service provided by the UK NCSC for UK public
sector organisations [PDNS]. In 2021, this service blocked access to
more than 160 million DNS queries (out of 602 billion total queries)
for the organisations signed up to the service [ACD2021]. This
included hundreds of thousands of queries for domains associated with
Flubot, Android malware that uses domain generation algorithms (DGAs)
to generate 25,000 candidate command and control domains each month -
these DGAs [DGAs] are a type of TTP.
IoCs such as malicious domains can be put on PDNS straight away and
can then be used to prevent access to those known malicious domains
across the entire estate of over 925 separate public sector entities
that use NCSC's PDNS. Coverage can be patchy with endpoints, as the
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roll-out of protections isn't uniform or necessarily fast - but if
the IoC is on PDNS, a consistent defence is maintained for devices
using PDNS, even if the device itself is not immediately updated.
This offers protection, regardless of whether the context is a BYOD
environment or a managed enterprise system. PDNS provides the most
front-facing layer of defence-in-depth solutions for its users, but
other IoCs, like Server Name Indication values in TLS or the server
certificate information, also provide IoC protections at other
layers.
Similar to the AV scenario, large scale services face risk decisions
around balancing threat against business impact from false positives.
Organisations need to be able to retain the ability to be more
conservative with their own defences, while still benefiting from
them. For instance, a commercial DNS filtering service is intended
for broad deployment, so will have a risk tolerance similar to AV
products; whereas DNS filtering intended for government users (e.g.
PDNS) can be more conservative, but will still have a relatively
broad deployment if intended for the whole of government. A
government department or specific company, on the other hand, might
accept the risk of disruption and arrange firewalls or other network
protection devices to completely block anything related to particular
threats, regardless of the confidence, but rely on a DNS filtering
service for everything else.
Other network defences can make use of this blanket coverage from
IoCs, like middlebox mitigation, proxy defences, and application
layer firewalls, but are out of scope for this draft. Large
enterprise networks are likely to deploy their own DNS resolution
architecture and possibly TLS inspection proxies, and can deploy IoCs
in these locations. However, in networks that choose not to, or
don't have the resources to, deploy these sorts of mitigations, DNS
goes through firewalls, proxies and possibly to a DNS filtering
service; it doesn't have to be unencrypted, but these appliances must
be able to decrypt it to do anything useful with it, like blocking
queries for known bad URIs.
Covering a broad range of IoCs gives defenders a wide range of
benefits: they are easy to deploy; they provide a high enough
confidence to be effective; at least some will be painful for
attackers to change; their distribution around the infrastructure
allows for different points of failure, and so overall they enable
the defenders to disrupt bad actors. The combination of these
factors cements IoCs as a particularly valuable tool for defenders
with limited resources.
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7. Security Considerations
This draft is all about system security. However, when poorly
deployed, IoCs can lead to over-blocking which may present an
availability concern for some systems. While IoCs preserve privacy
on a macro scale (by preventing data breaches), research could be
done to investigate the impact on privacy from sharing IoCs, and
improvements could be made to minimise any impact found. The
creation of a privacy-preserving IoC sharing method, that still
allows both network and endpoint defences to provide security and
layered defences, would be an interesting proposal.
8. Conclusions
IoCs are versatile and powerful. IoCs underpin and enable multiple
layers of the modern defence-in-depth strategy. IoCs are easy to
share, providing a multiplier effect on attack defence effort and
they save vital time. Network-level IoCs offer protection,
especially valuable when an endpoint-only solution isn't sufficient.
These properties, along with their ease of use, make IoCs a key
component of any attack defence strategy and particularly valuable
for defenders with limited resources.
For IoCs to be useful, they don't have to be unencrypted or visible
in networks - but crucially they do need to be made available, along
with their context, to entities that need them. It is also important
that this availability and eventual usage copes with multiple points
of failure, as per the defence-in-depth strategy, of which IoCs are a
key part.
9. IANA Considerations
This draft does not require any IANA action.
10. Acknowledgements
Thanks to all those who have been involved with improving cyber
defence in the IETF and IRTF communities.
11. Informative References
[ACD2021] UK NCSC, "Active Cyber Defence - The Fifth Full Year",
2022, <https://www.ncsc.gov.uk/files/ACD-The-Fifth-Year-
full-report.pdf>.
[ADS] Microsoft, "File Streams (Local File Systems)", 2018,
<https://docs.microsoft.com/en-us/windows/win32/fileio/
file-streams>.
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[ALIENVAULT]
AlienVault, "AlienVault", 2023,
<https://otx.alienvault.com/>.
[Annual2021]
UK NCSC, "Annual Review 2021", 2021,
<https://www.ncsc.gov.uk/files/
NCSC%20Annual%20Review%202021.pdf>.
[CISA] CISA, "Iranian Government-Sponsored APT Cyber Actors
Exploiting Microsoft Exchange and Fortinet Vulnerabilities
in Furtherance of Malicious Activities", 2021,
<https://www.cisa.gov/uscert/ncas/alerts/aa21-321a>.
[COBALT] Cobalt Strike, "Cobalt Strike", 2021,
<https://www.cobaltstrike.com/>.
[DFRONT] InfoSec Resources, "Domain Fronting", 2017,
<https://resources.infosecinstitute.com/topic/domain-
fronting/>.
[DGAs] MITRE, "Dynamic Resolution: Domain Generation Algorithms",
2020, <https://attack.mitre.org/techniques/T1483/>.
[FireEye] O'Leary, J., Kimble, J., Vanderlee, K., and N. Fraser,
"Insights into Iranian Cyber Espionage: APT33 Targets
Aerospace and Energy Sectors and has Ties to Destructive
Malware", 2017, <https://www.mandiant.com/resources/blog/
apt33-insights-into-iranian-cyber-espionage>.
[FireEye2] FireEye, "OVERRULED: Containing a Potentially Destructive
Adversary", 2018,
<https://www.mandiant.com/resources/blog/overruled-
containing-a-potentially-destructive-adversary>.
[GoldenTicket]
Soria-Machado, M., Abolins, D., Boldea, C., and K. Socha,
"Kerberos Golden Ticket Protection", 2014,
<https://cert.europa.eu/static/WhitePapers/UPDATED%20-
%20CERT-EU_Security_Whitepaper_2014-007_Kerberos_Golden_Ti
cket_Protection_v1_4.pdf>.
[KillChain]
Lockheed Martin, "The Cyber Kill Chain", 2020,
<https://www.lockheedmartin.com/en-us/capabilities/cyber/
cyber-kill-chain.html>.
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[LAZARUS] Kaspersky Lab, "Lazarus Under The Hood", 2018,
<https://media.kasperskycontenthub.com/wp-
content/uploads/sites/43/2018/03/07180244/
Lazarus_Under_The_Hood_PDF_final.pdf>.
[LITREVIEW]
Mulder, T. D., "Cyber Threat Intelligence Sharing: Survey
and Research Directions", 2018, <https://www.open-
access.bcu.ac.uk/7852/1/Cyber%20Threat%20Intelligence%20Sh
aring%20Survey%20and%20Research%20Directions.pdf>.
[Mimikatz] Mulder, J., "Mimikatz Overview, Defenses and Detection",
2016, <https://www.sans.org/reading-
room/whitepapers/detection/mimikatz-overview-defenses-
detection-36780>.
[MISP] MISP, "MISP", 2019, <https://www.misp-project.org/>.
[MISPCORE] MISP, "MISP Core", 2020, <https://github.com/MISP/misp-
rfc/blob/master/misp-core-format/raw.md.txt>.
[NCCGroup] Jansen, W., "Abusing cloud services to fly under the
radar", 2021, <https://research.nccgroup.com/2021/01/12/
abusing-cloud-services-to-fly-under-the-radar/>.
[NIST] US NIST, "Security control - Glossary", 2022,
<https://csrc.nist.gov/glossary/term/security_control>.
[OILRIG] Cimpanu, C., "Iranian hacker group becomes first known APT
to weaponize DNS-over-HTTPS (DoH)", 2020,
<https://www.zdnet.com/article/iranian-hacker-group-
becomes-first-known-apt-to-weaponize-dns-over-https-doh/>.
[OPENIOC] Gibb, W., "OpenIOC: Back to the Basics", 2013,
<https://www.fireeye.com/blog/threat-research/2013/10/
openioc-basics.html>.
[OPENNIC] OpenNIC Project, "OpenNIC Project", 2021,
<https://www.opennic.org/>.
[Owari] UK NCSC, "Owari botnet own-goal takeover", 2018,
<https://www.ncsc.gov.uk/report/weekly-threat-report-8th-
june-2018>.
[PDNS] UK NCSC, "Protective DNS", 2019,
<https://www.ncsc.gov.uk/information/pdns>.
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[PoP] Bianco, D.J., "The Pyramid of Pain", 2014,
<https://detect-respond.blogspot.com/2013/03/the-pyramid-
of-pain.html>.
[RFC7970] Danilyw, R., "The Incident Object Description Exchange
Format Version 2", 2016,
<https://datatracker.ietf.org/doc/html/rfc7970>.
[RULER] MITRE, "Ruler", 2020,
<https://attack.mitre.org/software/S0358/>.
[STIX] OASIS Cyber Threat Intelligence, "STIX", 2019,
<https://oasis-open.github.io/cti-documentation/stix/
intro>.
[Symantec] Symantec, "Elfin: Relentless", 2019,
<https://www.symantec.com/blogs/threat-intelligence/elfin-
apt33-espionage>.
[TAXII] OASIS Cyber Threat Intelligence, "TAXII", 2021,
<https://oasis-open.github.io/cti-documentation/taxii/
intro.html>.
[Timestomp]
OASIS Cyber Threat Intelligence, "Timestomp", 2019,
<https://attack.mitre.org/techniques/T1099/>.
[TLP] FIRST, "Traffic Light Protocol", 2021,
<https://www.first.org/tlp/>.
Authors' Addresses
Kirsty Paine
Splunk Inc.
Email: kirsty.ietf@gmail.com
Ollie Whitehouse
Binary Firefly
Email: ollie@binaryfirefly.com
James Sellwood
Email: james.sellwood.ietf@gmail.com
Andrew Shaw
UK National Cyber Security Centre
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Email: andrew.s2@ncsc.gov.uk
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