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Versions: 00 01 02 03                                                   
Internet Engineering Task Force                                 K. Paine
Internet-Draft                         UK National Cyber Security Centre
Intended status: Informational                             O. Whitehouse
Expires: January 13, 2022                                      NCC Group
                                                             J. Sellwood
                                                                  Twilio
                                                           July 12, 2021


    Indicators of Compromise (IoCs) and Their Role in Attack Defence
             draft-paine-smart-indicators-of-compromise-03

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 best practices of 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 new 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
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   This Internet-Draft will expire on January 13, 2022.

Copyright Notice

   Copyright (c) 2021 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents



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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   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  . . . . . . . . . . . . . . . . . . . . .   8
       3.2.3.  Sharing . . . . . . . . . . . . . . . . . . . . . . .   9
       3.2.4.  Deployment  . . . . . . . . . . . . . . . . . . . . .   9
       3.2.5.  Detection . . . . . . . . . . . . . . . . . . . . . .  10
       3.2.6.  Reaction  . . . . . . . . . . . . . . . . . . . . . .  10
       3.2.7.  End of Life . . . . . . . . . . . . . . . . . . . . .  10
   4.  Using IoCs Effectively  . . . . . . . . . . . . . . . . . . .  10
     4.1.  Opportunities . . . . . . . . . . . . . . . . . . . . . .  10
       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  . . . .  11
       4.1.3.  IoCs have a multiplier effect on attack defence
               effort  . . . . . . . . . . . . . . . . . . . . . . .  12
       4.1.4.  IoCs are easily shared  . . . . . . . . . . . . . . .  13
       4.1.5.  IoCs can provide significant time savings . . . . . .  13
       4.1.6.  IoCs allow for discovery of historic attacks  . . . .  13
       4.1.7.  IoCs can be attributed to specific threats  . . . . .  14
     4.2.  Case Studies  . . . . . . . . . . . . . . . . . . . . . .  14
       4.2.1.  Introduction  . . . . . . . . . . . . . . . . . . . .  14
       4.2.2.  Cobalt Strike . . . . . . . . . . . . . . . . . . . .  14
         4.2.2.1.  Overall TTP . . . . . . . . . . . . . . . . . . .  15
         4.2.2.2.  IoCs  . . . . . . . . . . . . . . . . . . . . . .  15
       4.2.3.  APT33 . . . . . . . . . . . . . . . . . . . . . . . .  15
         4.2.3.1.  Overall TTP . . . . . . . . . . . . . . . . . . .  16
         4.2.3.2.  IoCs  . . . . . . . . . . . . . . . . . . . . . .  16
   5.  Operational Limitations . . . . . . . . . . . . . . . . . . .  17
     5.1.  Time and Effort . . . . . . . . . . . . . . . . . . . . .  17



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       5.1.1.  Fragility . . . . . . . . . . . . . . . . . . . . . .  17
       5.1.2.  Discoverability . . . . . . . . . . . . . . . . . . .  18
     5.2.  Precision . . . . . . . . . . . . . . . . . . . . . . . .  19
       5.2.1.  Specificity . . . . . . . . . . . . . . . . . . . . .  19
       5.2.2.  Dual and Compromised Use  . . . . . . . . . . . . . .  19
     5.3.  Privacy . . . . . . . . . . . . . . . . . . . . . . . . .  20
     5.4.  Automation  . . . . . . . . . . . . . . . . . . . . . . .  21
   6.  Best Practice . . . . . . . . . . . . . . . . . . . . . . . .  21
     6.1.  Comprehensive Coverage and Defence-in-Depth . . . . . . .  21
     6.2.  Security Considerations . . . . . . . . . . . . . . . . .  24
   7.  Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .  24
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  24
   9.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  24
   10. Informative References  . . . . . . . . . . . . . . . . . . .  24
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  27

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.  There are various opportunities
   for defenders to implement controls based on IoCs and, equally,
   common operational limitations that are experienced.  This draft
   captures these considerations and provides best practice for
   implementers alongside two case studies which demonstrate the
   usefulness of IoCs for detecting and defending against real world
   attacks.  One case study involves an intrusion set (a collection of
   indicators for a specific attack) 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 intelligence practitioners) on these threats (for
   example, [Symantec] and [NCCGroup], respectively).

1.1.  Requirements Language

   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].

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.  Successful defence is




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   achieved through the blocking, monitoring and response to adversarial
   activity at a network, endpoint or application levels.

   Domain Generation Algorithm (DGA): used in malware strains to
   generate domain names periodically.  Adversaries may use DGAs to
   dynamically identify a destination for command and control traffic,
   rather than relying on a list of static IP addresses or domains that
   can be blocked more easily.

   Kill chain: a model for conceptually breaking down a cyber intrusion
   to allow 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 - their 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.

3.  IoC Fundamentals

3.1.  IoC Types and the Pyramid of Pain

   Indicators of Compromise (IoCs) are artefacts observed about 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 (blue
   teams) to pro-actively 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.  Examples of protocol-related
   IoCs can include:

   o  IPv4 and IPv6 addresses in network traffic.

   o  DNS domain names in network traffic, resolver caches or logs.

   o  TLS Server Name Indication values in network traffic.

   o  Code signing certificates in binaries or TLS certificate
      information (such as SHA256 hashes) in network traffic.

   o  Cryptographic hashes (e.g.  MD5, SHA1 or SHA256) of malicious
      binaries or scripts when calculated from network traffic or file
      system artefacts.



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   o  Attack tools (such as Mimikatz [Mimikatz]) and their code
      structure and execution characteristics.

   o  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).  Domain names are more specific than IP



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   addresses (as multiple domain names may be associated with a single
   IP address) and are more painful for an adversary to change.

   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.  Information on 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 June 2021, Alienvault contained:

   o  Groups (i.e., combinations of TTPs): 441

   o  Malware families (i.e., tools): ~24,000

   o  URL: 1,976,224

   o  Domain names: 34,959,787

   o  IPv4 addresses: 4,305,036

   o  SHA256 hash values: 4,767,891

   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, a contributing factor may be 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.



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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 often discovered initially through manual investigation or
   automated analysis.  They can be discovered in a range of sources
   (including in networks and at endpoints), but must either be
   extracted from logs monitoring protocol runs, code execution or
   system operations (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 yet unused 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 protocol run related to an
   attack is of limited benefit if indicators cannot be extracted and
   subsequently associated with a later related run of the same, or 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.

3.2.2.  Assessment

   Defenders do not all treat IoCs the same way, in part because they
   may assess the quality of the IoCs differently or because they may
   have differing needs or capabilities with which to deploy them.
   Defenders may, for example, place differing trust in IoCs depending
   on their source, freshness, confidence level, or the associated
   threat.  Significantly, to make such decisions defenders 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



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   actor it relates to, its role in an attack, the last time it was seen
   in use, its expected lifetime, or other related IoCs) is much more
   useful to a network defender, who can then 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 then shared
   at scale so many individuals and organisations can defend themselves.
   An IoC may be shared in isolation (other than 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], for 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)) provide paid
   intel 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 the Traffic Light Protocol [TLP]; at
   its simplest, this indicates that the receiver may share with anyone
   (TLP WHITE), share within the defined sharing community (TLP GREEN),
   share within their organisation (TLP AMBER), or not share with anyone
   outside the original specific IoC exchange (TLP RED).

3.2.4.  Deployment

   For IoCs to provide defence-in-depth (see Section 6.1), which is one
   of their key strengths, and so cope with different points of failure,
   they should be deployed in controls monitoring networks and endpoints
   through solutions that have sufficient privilege 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 intel feeds to the appropriate security controls.








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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.

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 command and control 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 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.









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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
   all 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.  Further, IoCs relevant to a specific kill chain may
   only reflect activity performed during a certain phase and so
   contribute to complete coverage of the kill chain as part of an
   intrusion set.

   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 indicator 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



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   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 less 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.
   One reason for this is that use of IoCs does not require the same
   intensive training as needed for more subjective controls, such as
   those based on manual analysis of tipped machine learning events.  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.

4.1.3.  IoCs have a multiplier effect on attack defence effort

   Individual IoCs can provide widespread protection that scales
   effectively for defenders.  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.  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.  The prime contractor
   from our earlier example can supply IoCs to the small subcontractor
   and so further uplift that smaller entities' defensive capability and
   at the same time protect itself and its interests.

   Not only may multiple organisations benefit through directly
   receiving shared IoCs, 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 additional effort.








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4.1.4.  IoCs are easily shared

   There is significant benefit to be had from the sharing of IoCs and
   they can be easily shared for two main reasons: firstly, indicators
   are easy to distribute as they are textual and so in small numbers
   are frequently exchanged in emails, blog posts, or technical reports;
   secondly, standards such as MISP Core [MISPCORE], OpenIOC [OPENIOC],
   and STIX [STIX] provide well-defined formats for sharing large
   collections or regular sets of IoC along with all the associated
   context.  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.

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 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,



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   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 and so allows them the technical freedom and
   capability to choose their risk posture and defence methods.
   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.  To more complicated
   approaches involving clustering similar, but not identical,
   combinations of TTPs seen across multiple campaigns over months or
   years of activity, in concert with detailed malware reverse engineer
   and target profiling all overlaid on a geopolitical and criminal
   backdrop to infer attribution to a single distinct threat actor
   (whether identifiable in the real world or not).

4.2.  Case Studies

4.2.1.  Introduction

   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.

4.2.2.  Cobalt Strike

   Cobalt Strike [COBALT] is a commercial attack framework 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.  The proprietary beacon
   supports TLS encryption overlaid with a custom encryption scheme
   based on a public-private keypair.  The product also supports other




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   techniques, such as domain fronting [DFRONT], in attempt to avoid
   obvious passive detection by static network signatures.

4.2.2.1.  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.

4.2.2.2.  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:

   o  IP addresses of the C2 servers

   o  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.

4.2.3.  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.






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4.2.3.1.  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, a freely
   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.

4.2.3.2.  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:

   o  9 hashes and email subject lines

   o  5 IP addresses

   o  7 domain names






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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, when
   thinking in terms of fragility, 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 happens
   less frequently due to the increased pain of managing infrastructure
   compared to altering files, and so support 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 may use after initial
   access; and that may in turn be more fragile than the file hash of a
   post-exploitation reconnaissance tool the attacker controls using
   that remote access trojan but that doesn't directly connect 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 in
   relation to another.





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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).

   Finally, it is worth noting that fragility can apply to an entire
   class of IoC for a range of reasons; for example, IPv4 addresses are
   becoming increasingly fragile due to addresses growing scarce,
   widespread use of cloud services, and the ease with which domain
   names can be moved from one hosting provider to another (thus
   changing IP range).

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.



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   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
   complicated to identify.  Further, some malwares obfuscate their
   intended destinations by using alternative DNS resolution services
   (e.g., OpenNIC [OPENNIC]) or by performing transformation operations
   on resolved IP addresses to determine the real C2 server address
   encoded in the DNS response [LAZARUS].

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
   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



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   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.

   In a similar manner, 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
   that employee's usual working hours.

   It is reasons such as these that context is so important when sharing
   and using IoCs.

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.

   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 appears often 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.  Research
   opportunities exist to determine how IoC sharing groups' requirements
   for trust and members' interaction strategies vary and whether



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   sharing can be optimised or incentivised, such as by using game
   theoretic approaches.

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.

   The adoption of automation can also enable faster and easier
   correlation of IoC detections across log sources, time, and space.
   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), 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 or
   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.  Best Practice

6.1.  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 cycle between high precision but high fragility options
   and more robust but less precise indicators.  As fragile indicators
   are changed, the more robust IoCs allow for continued detection and
   faster rediscovery.  This is why it's important to collect as many
   IoCs as possible across the whole PoP.




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   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
   nderstand 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
   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 (see [EVOLVE]),
   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 you hope an endpoint solution will pick it
   up.  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
   against compromised endpoints when these IoCs used to detect the
   attack in network traffic, even if the compromise 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 IoCs provide a layer of a defence-in-depth
   solution is Protective DNS (PDNS), a free and voluntary DNS filtering
   service provided by the UK NCSC for UK public sector organisations
   [PDNS].  In 2018, this service blocked access to 57.4 million DNS



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   queries for 118,527 unique reasons (out of 68.7 billion total
   queries) for the organisations signed up to the service [ACD2019]. 28
   million of them were for domain generation algorithms (DGAs) [DGAs],
   including 15 known DGAs which 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 460 separate public sector entities
   that use NCSC's PDNS [Annual2019].  Coverage can be patchy with
   endpoints, as the roll-out of protections isn't uniform or
   necessarily fast - but if the IoC is on PDNS, a consistent defence is
   maintained.  This offers protection, regardless of whether the
   context is a BYOD environment or a managed enterprise system.  Other
   IoCs, like Server Name Indicator values in TLS or the server
   certificate information, also provide IoC protections.

   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 they're out of scope for this draft.  Note too
   that 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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6.2.  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.

7.  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.

8.  IANA Considerations

   This draft does not require any IANA action.

9.  Acknowledgements

   Thanks to all those who have been involved with improving cyber
   defence in the IETF and IRTF communities.

10.  Informative References

   [ACD2019]  Levy, I. and M. S, "Active Cyber Defence - The Second
              Year", 2019, <https://www.ncsc.gov.uk/report/active-cyber-
              defence-report-2019>.

   [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", 2021,
              <https://otx.alienvault.com/>.

   [Annual2019]
              NCSC, "Annual Review 2019", 2019,
              <https://www.ncsc.gov.uk/annual-review/2019/ncsc/docs/
              ncsc_2019-annual-review.pdf>.

   [COBALT]   Cobalt Strike, "OVERRULED: Containing a Potentially
              Destructive Adversary", 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/>.

   [EVOLVE]   McFadden, M., "Evolution of Endpoint Security - An
              Operational Perspective", 2021,
              <https://datatracker.ietf.org/doc/draft-mcfadden-opsec-
              endp-evolve/>.

   [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.fireeye.com/blog/threat-
              research/2017/09/apt33-insights-into-iranian-cyber-
              espionage.html>.

   [FireEye2]
              FireEye, "OVERRULED: Containing a Potentially Destructive
              Adversary", 2018, <https://www.fireeye.com/blog/threat-
              research/2018/12/overruled-containing-a-potentially-
              destructive-adversary.html>.

   [GoldenTicket]
              Soria-Machado, M., Abolins, D., Boldea, C., and K. Socha,
              "Kerberos Golden Ticket Protection", 2014,
              <https://cert.europa.eu/static/WhitePapers/UPDATED - CERT-
              EU_Security_Whitepaper_2014-007_Kerberos_Golden_Ticket_Pro
              tection_v1_4.pdf>.







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   [KillChain]
              Lockheed Martin, "The Cyber Kill Chain", 2020,
              <https://www.lockheedmartin.com/en-us/capabilities/cyber/
              cyber-kill-chain.html>.

   [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>.

   [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/>.

   [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]    NCSC, "Owari botnet own-goal takeover", 2018,
              <https://www.ncsc.gov.uk/report/weekly-threat-report-8th-
              june-2018>.

   [PDNS]     NCSC, "Protective DNS", 2019,
              <https://www.ncsc.gov.uk/information/pdns>.

   [PoP]      Bianco, D., "The Pyramid of Pain", 2014, <https://detect-
              respond.blogspot.com/2013/03/the-pyramid-of-pain.html>.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.



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   [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
   UK National Cyber Security Centre

   Email: kirsty.p@ncsc.gov.uk


   Ollie Whitehouse
   NCC Group

   Email: ollie.whitehouse@nccgroup.com


   James Sellwood
   Twilio

   Email: jsellwood@twilio.com













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