RFC9424: Indicators of Compromise (IoCs) and Their Role in Attack Defence
Download in text format
Related keywords:
(Attack Defence)
(IOC)
Internet Engineering Task Force (IETF) K. Paine
Request for Comments: 9424 Splunk Inc.
Category: Informational O. Whitehouse
ISSN: 2070-1721 Binary Firefly
J. Sellwood
A. Shaw
UK National Cyber Security Centre
August 2023
Indicators of Compromise (IoCs) and Their Role in Attack Defence
Abstract
Cyber defenders frequently rely on Indicators of Compromise (IoCs) to
identify, trace, and block malicious activity in networks or on
endpoints. This document 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 document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9424.
Copyright Notice
Copyright (c) 2023 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
(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
2. Terminology
3. IoC Fundamentals
3.1. IoC Types and the Pyramid of Pain
3.2. IoC Lifecycle
3.2.1. Discovery
3.2.2. Assessment
3.2.3. Sharing
3.2.4. Deployment
3.2.5. Detection
3.2.6. Reaction
3.2.7. End of Life
4. Using IoCs Effectively
4.1. Opportunities
4.1.1. IoCs underpin and enable multiple layers of the modern
defence-in-depth strategy.
4.1.2. IoCs can be used even with limited resources.
4.1.3. IoCs have a multiplier effect on attack defence efforts
within an organisation.
4.1.4. IoCs are easily shared between organisations.
4.1.5. IoCs can provide significant time savings.
4.1.6. IoCs allow for discovery of historic attacks.
4.1.7. IoCs can be attributed to specific threats.
4.2. Case Studies
4.2.1. Cobalt Strike
4.2.1.1. Overall TTP
4.2.1.2. IoCs
4.2.2. APT33
4.2.2.1. Overall TTP
4.2.2.2. IoCs
5. Operational Limitations
5.1. Time and Effort
5.1.1. Fragility
5.1.2. Discoverability
5.1.3. Completeness
5.2. Precision
5.2.1. Specificity
5.2.2. Dual and Compromised Use
5.2.3. Changing Use
5.3. Privacy
5.4. Automation
6. Comprehensive Coverage and Defence-in-Depth
7. IANA Considerations
8. Security Considerations
9. Conclusions
10. Informative References
Acknowledgements
Authors' Addresses
1. Introduction
This document describes the various types of IoCs 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 document provides suggestions for implementers of
controls based on IoCs as well as potential operational limitations.
Two case studies that 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 involves 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 of, detection of, and response to attempted and
successful cyber intrusions. A successful defence can be achieved
through blocking, monitoring, and responding to adversarial
activity at the 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".
Domain Generation Algorithm (DGA):
The algorithm 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 against
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 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
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 the 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
* 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],
that 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. The position of
each IoC type 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 in understanding and discussing intrusion sets
most relevant to them.
/\
/ \ 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
nonintrusive 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
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 provides 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, 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 AlienVault, an open threat intelligence community
[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
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 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 determine 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 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, simply log it, actively monitor
it, or outright 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], Malware Information
Sharing Platform (MISP) core [MISPCORE], OpenIOC [OPENIOC], and
Incident Object Description Exchange Format (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). Whoever 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).
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 achieved 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
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 that 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 (IDSs), and Intrusion
Prevention Systems (IPSs) all employ IoCs to identify and mitigate
threats across networks. Antivirus (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.
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 of the executable file
remains the same, and this hash can be deployed as an IoC 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 lookups 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. However, should these preemptive measures fail,
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 that
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.
4.1.3. IoCs have a multiplier effect on attack defence efforts 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 thus further uplift that
smaller entity's defensive capability while protecting itself and its
interests at the same time.
Multiple organisations may benefit from directly receiving shared
IoCs (see Section 4.1.4), but they may also benefit from 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 organisations or
additional effort.
4.1.4. IoCs are easily shared between organisations.
IoCs can also be very easily shared between individuals and
organisations. First, 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. Second, standards, such as those mentioned in
Section 3.2.3, exist to provide well-defined formats for sharing
large collections or regular sets of IoCs along with all the
associated context. While discovering one IoC can be intensive, once
shared via well-established routes, that individual IoC may protect
thousands of organisations and thus all of the users in those
organisations. 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 hence change the relevant IoCs.
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, legacy systems 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 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 it 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
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, for example, 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), a
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 an 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 with traffic originating from a network that is already
communicating with a non-malicious domain regularly 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 licence
watermark.
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, their beacon configurations to be
downloaded, and the associated infrastructure addresses to be
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 shows that 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 and 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 the 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
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 that 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 that 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 first attempt to spread the wiper
manually or use worm-like capabilities against unpatched
vulnerabilities on the networked computers.
4.2.2.2. IoCs
As a result of investigations by a partnership of the 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 the 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 and sharing further IoCs:
* 8 hashes of malicious executables
* 3 IP addresses
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 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 PoP 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. First,
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. Second, 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. Third, 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.
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. However, if 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 within either the payload (e.g.,
within its source code or associated resource) or the infected
endpoint's file system (e.g., using alternative data streams [ADS]),
thus requiring more effort to discover. Further, the attacker may be
storing the configuration in memory only or relying on a 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
complicated to identify. Further, some malwares obfuscate their
intended destinations by using alternative DNS resolution services
(e.g., OpenNIC [OPENNIC]), by using 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 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 made 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 that 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
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 is regularly
reassigned from one user to another; 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 or not the defender organisation usually uses
that tool for legitimate system administration. Notably, such dual-
use indicators are context specific, considering both whether they
are usually used legitimately and how 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.
For reasons like these, context is very 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 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.
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 and with whom to share about a cyber
intrusion. 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 that 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.
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. Thus, 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 PoP's
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, so when tuned appropriately, they
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 Antivirus (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, that a defence-in-
depth approach is commonly advised, 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 particularly 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 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
roll-out of protections isn't uniform or necessarily fast. However,
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 it 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 document. 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 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; and 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.
7. IANA Considerations
This document has no IANA actions.
8. Security Considerations
This document 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 method of sharing IoCs that still
allows both network and endpoint defences to provide security and
layered defences would be an interesting proposal.
9. 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 efforts, and
they save vital time. Network-level IoCs offer protection, which is
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 it is crucial that they be made available, along
with their context, to entities that need them. It is also important
that this availability and eventual usage cope with multiple points
of failure, as per the defence-in-depth strategy, of which IoCs are a
key part.
10. Informative References
[ACD2021] UK NCSC, "Active Cyber Defence - The Fifth Year", May
2022, <https://www.ncsc.gov.uk/files/ACD-The-Fifth-Year-
full-report.pdf>.
[ADS] Microsoft, "File Streams (Local File Systems)", January
2021, <https://docs.microsoft.com/en-
us/windows/win32/fileio/file-streams>.
[ALIENVAULT]
AlienVault, "AlienVault: The World's First Truly Open
Threat Intelligence Community",
<https://otx.alienvault.com/>.
[Annual2021]
UK NCSC, "NCSC Annual Review 2021: Making the UK the
safest place to live and work online", 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", November 2021,
<https://www.cisa.gov/uscert/ncas/alerts/aa21-321a>.
[COBALT] "Cobalt Strike", <https://www.cobaltstrike.com/>.
[DFRONT] Infosec, "Domain Fronting", April 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", September 2017,
<https://www.mandiant.com/resources/blog/apt33-insights-
into-iranian-cyber-espionage>.
[FireEye2] Ackerman, G., Cole, R., Thompson, A., Orleans, A., and N.
Carr, "OVERRULED: Containing a Potentially Destructive
Adversary", December 2018,
<https://www.mandiant.com/resources/blog/overruled-
containing-a-potentially-destructive-adversary>.
[GoldenTicket]
Mizrahi, I. and Cymptom, "Steal or Forge Kerberos Tickets:
Golden Ticket", 2020,
<https://attack.mitre.org/techniques/T1558/001/>.
[KillChain]
Lockheed Martin, "The Cyber Kill Chain",
<https://www.lockheedmartin.com/en-us/capabilities/cyber/
cyber-kill-chain.html>.
[LAZARUS] Kaspersky Lab, "Lazarus Under The Hood",
<https://media.kasperskycontenthub.com/wp-
content/uploads/sites/43/2018/03/07180244/
Lazarus_Under_The_Hood_PDF_final.pdf>.
[LITREVIEW]
Wagner, T., Mahbub, K., Palomar, E., and A. Abdallah,
"Cyber Threat Intelligence Sharing: Survey and Research
Directions", January 2019, <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",
February 2016, <https://www.sans.org/white-papers/36780/>.
[MISP] "MISP", <https://www.misp-project.org/>.
[MISPCORE] Dulaunoy, A. and A. Iklody, "MISP core format", Work in
Progress, Internet-Draft, draft-dulaunoy-misp-core-format-
16, 26 February 2023,
<https://datatracker.ietf.org/doc/html/draft-dulaunoy-
misp-core-format-16>.
[NCCGroup] Jansen, W., "Abusing cloud services to fly under the
radar", January 2021,
<https://research.nccgroup.com/2021/01/12/abusing-cloud-
services-to-fly-under-the-radar/>.
[NIST] NIST, "Glossary - security control",
<https://csrc.nist.gov/glossary/term/security_control>.
[OILRIG] Cimpanu, C., "Iranian hacker group becomes first known APT
to weaponize DNS-over-HTTPS (DoH)", August 2020,
<https://www.zdnet.com/article/iranian-hacker-group-
becomes-first-known-apt-to-weaponize-dns-over-https-doh/>.
[OPENIOC] Gibb, W. and D. Kerr, "OpenIOC: Back to the Basics",
October 2013, <https://www.fireeye.com/blog/threat-
research/2013/10/openioc-basics.html>.
[OPENNIC] "OpenNIC", <https://www.opennic.org/>.
[Owari] UK NCSC, "Owari botnet own-goal takeover", 2018, <https://
webarchive.nationalarchives.gov.uk/ukgwa/20220301141030/
https://www.ncsc.gov.uk/report/weekly-threat-report-8th-
june-2018>.
[PDNS] UK NCSC, "Protective Domain Name Service (PDNS)", August
2017, <https://www.ncsc.gov.uk/information/pdns>.
[PoP] Bianco, D., "The Pyramid of Pain", March 2013,
<https://detect-respond.blogspot.com/2013/03/the-pyramid-
of-pain.html>.
[RFC7970] Danyliw, R., "The Incident Object Description Exchange
Format Version 2", RFC 7970, DOI 10.17487/RFC7970,
November 2016, <https://www.rfc-editor.org/info/rfc7970>.
[RULER] MITRE, "Ruler",
<https://attack.mitre.org/software/S0358/>.
[STIX] OASIS Cyber Threat Intelligence (CTI), "Introduction to
STIX", <https://oasis-open.github.io/cti-
documentation/stix/intro>.
[Symantec] Symantec, "Elfin: Relentless Espionage Group Targets
Multiple Organizations in Saudi Arabia and U.S.", March
2019, <https://www.symantec.com/blogs/threat-intelligence/
elfin-apt33-espionage>.
[TAXII] OASIS Cyber Threat Intelligence (CTI), "Introduction to
TAXII", <https://oasis-open.github.io/cti-
documentation/taxii/intro.html>.
[Timestomp]
MITRE, "Indicator Removal: Timestomp", January 2020,
<https://attack.mitre.org/techniques/T1099/>.
[TLP] FIRST, "Traffic Light Protocol (TLP)",
<https://www.first.org/tlp/>.
Acknowledgements
Thanks to all those who have been involved with improving cyber
defence in the IETF and IRTF communities.
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
Email: andrew.s2@ncsc.gov.uk