The Quest for the Appropriate Cyber-threat Intelligence Sharing
Platform
*
Thanasis Chantzios
1
, Paris Koloveas
1
, Spiros Skiadopoulos
1
, Nikos Kolokotronis
1
,
Christos Tryfonopoulos
1
, Vasiliki-Georgia Bilali
2
and Dimitris Kavallieros
2
1
Department of Informatics and Telecommunications, University of the Peloponnese, Tripolis, Greece
2
KEMEA Center for Security Studies, Ministry of Citizen Protection, Athens, Greece
Keywords:
Cyber-threat, Intelligence, Sharing.
Abstract:
Cyber-threat intelligence (CTI) is any information that can help an organization identify, assess, monitor, and
respond to cyber-threats. It relates to all cyber components of an organization such as networks, computers,
and other types of information technology. In the recent years, due to the major increase of cyber-threats,
CTI sharing is becoming increasingly important both as a subject of research and as a concept of providing
additional security to organizations. However, selecting the proper tools and platforms for CTI sharing, is a
challenging task, that pertains to a variety of aspects. In this paper, we start by overviewing the CTI procedure
(threat types, categories, sources and the general CTI life-cycle). Then, we present a set of seven high-
level CTI plaftorm recommendations that can be used to evaluate a platform and subsequently we survey six
state-of-the-art cyber-threat intelligence platforms. Finally, we compare and evaluate the six aforementioned
platforms by means of the earlier proposed recommendations.
1 INTRODUCTION
Cyber-threat Intelligence (CTI) is any information
that can help an organization identify, assess, monitor,
and respond to cyber-threats. Examples of such infor-
mation include indicators (system artifacts or observ-
ables associated with an attack), security alerts, threat
intelligence reports, as well as recommended security
tool configurations.
Most organizations already produce an enormous
amount of CTI in multiple forms and types (Dalziel,
2015). It is crucial for effective cyber-defense to share
such intelligence and information. Specifically, CTI
sharing provides increased awareness, improved se-
curity posture, knowledge maturing, and increased
defensive agility.
In this paper, we evaluate the appropriateness of
different vulnerability reporting frameworks for dis-
seminating the identified cyber-threats across differ-
ent organizations to promote awareness about emerg-
ing cyber-threats. Moreover, we investigate issues
pertaining to the basic structure, the key elements
*
This work was funded by EU’s Horizon 2020 research and
innovation program under grant agreement no. 786698 and
reflects only the authors’ view.
(i.e., expressiveness, flexibility, extensibility, automa-
tion, structuring), and prominent strengths and weak-
nesses of the presented frameworks.
Our goal is to survey related tools and plat-
forms, evaluate them and identify the most appropri-
ate for cyber-threat intelligence sharing, similarly to
the work carried out in (Farnham, 2013), in which
the author surveys leading tools and standards for CTI
systems. In a nutshell, this work:
Overviews CTI sharing. Specifically, we illustrate
the corresponding CTI types (indicators, tactics,
alerts, etc.), review possible CTI sources, and de-
tail the CTI processing and sharing cycle. More-
over, we discuss the benefits of CTI sharing and
present the challenges of CTI sharing.
Presents a set of seven high-level recommenda-
tions that may serve as requirements for a CTI
sharing platform.
Surveys six state-of-the-art CTI sharing plat-
forms, namely MISP, GOSINT, OpenTPX, YETI,
OpenTAXII and CIF.
Compares and critically evaluates the six afore-
mentioned sharing platforms.
This paper is structured as follows. In Section
Chantzios, T., Koloveas, P., Skiadopoulos, S., Kolokotronis, N., Tryfonopoulos, C., Bilali, V. and Kavallieros, D.
The Quest for the Appropriate Cyber-threat Intelligence Sharing Platform.
DOI: 10.5220/0007978103690376
In Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA 2019), pages 369-376
ISBN: 978-989-758-377-3
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
369
2 we offer an overview on CTI sharing focusing on
threat information types, CTI categories, the CTI cy-
cle and the CTI sharing requirements. In Section 3
we survey six state-of-the-art CTI sharing platforms
and in Section 4 we review and evaluate them. Our
conclusions are illustrated in Section 5.
2 CTI SHARING OVERVIEW
Over the decades, cyber-threats have grown, morphed
and become more sophisticated. Adversaries may
now use a vast set of tools and tactics in order to at-
tack their victims. Their motivations range from in-
telligence collection to service destruction and from
reputation acquirement to financial gain. Nowadays,
understanding the attacker is an increasingly compli-
cated and highly important task required for security
assurance. The way to identify and understand an at-
tacker as well as the use of that information to pro-
tect organizations and infrastructures is a fundamen-
tal concept behind cyber-threat intelligence. Threat
intelligence is focused on the analysis of the capabil-
ities, motivations, and the goals of an adversary; and
CTI is focused on how these goals are achieved using
the cyber domains (Deloitte, 2015).
Cyber-threat information is any kind of informa-
tion that could help an organization protect itself
against any threat and detect the activities of an ad-
versary. There are many types of threat information
that may include:
Indicators. These are observables or technical arti-
facts suggesting that an attack is going to happen or
that a compromise of the system has already occurred.
Indicators can be used in a system to protect it against
any potential threats. Examples of indicators include
the IP address of a suspicious command, a distrustful
DNS domain name, a URL that references suspicious
content, a file hash using a malicious executable, or
text code of a malicious email message.
Tactics, Techniques, and Procedures (TTPs).
These elements are used to describe the behavior of an
adversary. Specifically, tactics are descriptions of be-
havior, techniques are descriptions of tactics and pro-
cedures are detailed descriptions in the context of a
technique. TTPs describe the willingness of an adver-
sary to use a specific attacking tool, a malware variant,
an exploit or a delivery mechanism (e.g., phishing).
Security Alerts. Also known as advisories, security
alerts are brief and human-readable notifications re-
garding vulnerabilities, exploits or security issues.
Threat Intelligence Reports. These include docu-
ments that describe TTPs, types of systems, adver-
saries and target information, as well as any other
Figure 1: The CTI life-cycle.
information related to cyber-threats that provide en-
hanced awareness to an organization.
Tool Configurations. These include recommenda-
tions for the installation and the use of mechanisms
in order to collect, process, exchange and analyze
CTI. Tool configuration information may consist of
instructions on how to customize and use intrusion
detection signatures, web filter configuration files, or
firewall rules.
CTI sources can be categorized into three different
categories: internal, community, and external (Fried-
man and Bouchard, 2015), and are briefly explained
below:
Internal Sources. In this group of sources, CTI is
collected from an intra-organization level. This may
include reported information from security tools like
intrusion prevention systems (IPS), firewalls, host se-
curity systems (anti-virus), etc. In addition, a signif-
icant internal source of CTI derives from computer
forensic analysis, which provides information about
application settings, running processes, services used,
system events, and so on, and could indicate adversar-
ial behavior as well.
Community Sources. These include CTI shared via
a trusted relationship with multiple members having
shared interests. This can be an informal group with
member organizations that are in the same industry
sector or that have other common interests. The infor-
mation sharing and analysis centers are such an exam-
ple (ENISA, 2018). They are non-profit organizations
providing a central resource for gathering CTI and al-
lowing two-way sharing between the private and the
public sector.
External Sources. This group contains CTI gathered
outside of an organization. External sources may be
distinguished into there categories. (a) Public that are
provided freely and are based on volunteered data. (b)
Private that require paid subscription and offer guar-
anties for data quality and credibility. (c) Unindexed
that include sites accessible only from the deep or the
dark web (e.g., chatrooms, pastebins, forums, market-
places)
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
370
The CTI cycle, illustrated in Figure 1, is the pro-
cess of generating and evaluating CTI. The first step
of this process is CTI source identification (or direc-
tion). It pertains to the identification of threat in-
formation that needs to be collected from monitor-
ing devices, feeds, and security repositories to support
decision-making and raise cyber-security awareness.
The next step, CTI gathering, is the collection of
the necessary data from the identified sources, along
with the tools for extracting a wide variety of infor-
mation, like tactical information (infrastructure, mal-
ware, and exploits) and strategic information (reveal-
ing attackers’ goals). This process requires a series
of steps starting from the collection of relevant IP ad-
dresses. Moreover, it is not a one-time action, but,
it should be performed in a continuous manner. The
main goal at this stage is to collect as much infor-
mation as possible and allow correlations and further
analysis.
The third step is CTI analysis and is built upon the
information that has being collected; it includes both
automated and human means of analysis.
The fourth step is CTI sharing to the relevant
stakeholders, i.e., the entities that can utilize the gen-
erated intelligence, in a form that they find to be
appropriate, useful, and in many cases actionable.
This makes sharing highly-dependent on the audience
(e.g., tactical, operational, and strategic level).
CTI review (also referred to as CTI feedback),
which is the last step in the above process, constitutes
the key to the continuous improvement of the gener-
ated intelligence.
Threat information sharing provides access to
threat information that in a different case might be
unavailable to an organization. There is a plethora
of benefits in sharing CTI that include:
Increased Situational Awareness. Using shared re-
sources enhances security by leveraging the capabil-
ities (e.g., knowledge, experience) of partners in a
proactive way.
Improved Security Posture. It has become easier
for organizations to identify affected systems, imple-
ment measures for protection, enhance their detection
methods and recover from attacks.
Knowledge Maturing. This process increases the
value of information by enriching existing indicators
and developing knowledge of adversary TTPs that are
associated with a specific incident, threat, or threat
campaign.
Increased Defensive Agility. Threat adversaries con-
tinuously adapt their TTPs and try to evade detec-
tion, security controls, and exploit new vulnerabili-
ties. In order to reduce the probability of success-
ful attacks, organizations are often informed about
changing TTPs and usually detect and respond to
threats rapidly.
Sharing threat intelligence information has nu-
merous benefits; still it also raises several challenges
that relate to the production and consumption of threat
information that have to be addressed. In the follow-
ing, we outline some important challenges; see also
(NIST, 2016) for a thorough discussion of the topic.
Establishing Trust. Trust relationships create the ba-
sics for information sharing, but their maintenance re-
quires significant effort. To achieve that, all members
participating in such relationships, should be provided
with a common mechanism that is capable of preserv-
ing and continuously monitoring a trust model for the
community.
Achieving Interoperability and Automation. The
use of standardized data formats is an important build-
ing block for interoperability because it allows orga-
nizations and repositories to easily exchange threat in-
formation in a standardized way. However, an organi-
zation might require significant time and resources to
adopt to new data formats.
Securing Sensitive Information. Publishing sensi-
tive information such as controlled unclassified data
and personally identifiable information may result in
violating sharing agreements, and loss of reputation,
or even financial loss. Organizations should imple-
ment policies and technical controls that disallow the
disclosure of sensitive data.
Enabling Information Sharing. All organizations
that are willing to publish and consume threat intelli-
gence information need related tools and well-trained
personnel. Typically, tools are responsible for in-
formation sharing (intelligence publication and con-
sumption) while personnel focus on analysis, decision
and action making.
The above is an indicative list of CTI sharing chal-
lenges and it is by no means complete. Several other
issues need to be resolved. For instance, how to ac-
cess external sources and incorporate actionable CTI,
how to estimate the quality of the received CTI and
how to provide CTI, how to comply with policies or
requirements pertaining to privacy and the limitation
of attribution should also be addressed (Brown et al.,
2015; NIST, 2016).
We conclude this section by presenting recom-
mendations of CTI sharing aspects by relying on a
set of high-level requirements, and by considering the
findings of desk research on the current situation on
CTI sharing (Dandurand and Serrano, 2013; Sauer-
wein et al., 2017).
Requirement 1: The sharing mechanism must allow
CTI sharing between the platform and different stake-
holders (like service providers and certified authori-
The Quest for the Appropriate Cyber-threat Intelligence Sharing Platform
371
ties).
Requirement 2: The sharing mechanism must allow
CTI sharing between the platform and the end-users’
devices.
Requirement 3: The sharing mechanism and plat-
form should be expressible, flexible, and scalable.
Requirement 4: The sharing mechanism (and plat-
form) should allow information to be both human and
machine readable and facilitate automation.
Requirement 5: The sharing platform should allow
storing information about the source of CTI.
Requirement 6: The sharing platform should support
information filtering and alerting.
Requirement 7: The sharing platform should be
open source.
3 CTI SHARING PLATFORMS
The complexity of modern infrastructures leads to
a cyber-threat landscape of growing sophistication
and complexity, where cyber-security incidents occur
with increasing frequency. This fact necessitates ef-
ficient and automated tools for analyzing and shar-
ing heterogeneous CTI related to the present systems’
configurations, attacker’s threats and tactics, indica-
tors of ongoing incidents, and so on, in order to build
proper and effective defensive capabilities. Given the
numerous architectures, products and systems being
used as sources of data for information sharing sys-
tems, standardized and structured CTI representations
are required to allow a satisfying level of interoper-
ability across the various stakeholders.
As highlighted in several works (Hernandez-
Ardieta et al., 2013; Skopik, 2018) considerable ef-
forts have been put during the last decade to standard-
ize the data formats and exchange protocols related
to CTI. The initiative led by MITRE, referred to as
making security measurable (MSM)
1
, constitutes the
most prominent such effort along with the more recent
initiatives of ENISA towards improving cyber-threat
information sharing among the Computer Emergency
Response Teams (CERTs), Computer Security Inci-
dent Response Teams (CSIRTs), Law Enforcement
Agencies (LEAs), and other relevant stakeholders
(ENISA, 2013; ENISA, 2015; ENISA, 2016). An
overview of existing efforts is presented in Figure 2,
where standards are classified into different areas. As
we can see there is a wide variability in the areas
covered by the standards that include: configuration
guidance, vulnerability alerts, treat alerts, risk/attack
indicators and so on. Furthermore, some of these
1
https://msm.mitre.org
Figure 2: Different vulnerability areas covered by existing
standards (Skopik, 2018).
standards, define how CTI should be outlined, on
the matter of which information is useful and should
be encompassed by the CTI sharing paradigm. For
example, STIX (Structured Threat Information eX-
pression) belongs in three vulnerability areas (namely
threat alerts, risk/attack indicators, and incident re-
ports), since it encapsulates information about attack
patterns, courses of action, vulnerabilities, reports,
and more (OASIS-Open, 2018).
The need of assessment, detection and gathering
cyber-threat information escalated over the years; this
is also demonstrated by the ENISA threat landscape
report of 2017 (ENISA, 2017). Specifically, the sur-
vey of ENISA indicates that 9 cyber-threats out of
the top 15 had an increased trend factor in 2016,
whereas in the 2017, 11 cyber-threats’ trend had been
increased. That translates to a 13% increase of cyber-
threats in one year. To address the increasing CTI
needs, the formats and languages discussed in the be-
ginning of this section, were realized into functional
platforms. In the sequel, we outline six platforms
and tools (namely MISP, GOSINT, OpenTPX, YETI,
OpenTAXII, and CIF) that implement the aforemen-
tioned frameworks and language platforms for CTI
sharing, also used by ENISA.
The Malware Information Sharing Platform
(MISP). One of the most widespread CTI sharing
platforms is Malware Information Sharing Platform
(MISP) (Wagner et al., 2016). MISP is an open
source threat intelligence and open standard for threat
information sharing platform, which detects, stores
and shares technical and non-technical information
about malware samples, incidents, attackers and in-
telligence (MISP-Community, 2019). Specifically,
MISP provides a User Interface (UI), which enables
users to create, search or share events amongst other
MISP users or communities. Furthermore, all CTI
stored in the MISP database can be accessed through
an API, which allows for data exporting in a wide
variety of formats, such as XML, JSON, OpenIOC,
STIX, and more.
Additionally, MISP has an automatic correlation
mechanism that is able to identify relationships be-
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
372
tween attributes, objects and indicators from malware
correlation engines. Moreover, MISP stores data in
a structured format, provides extensive support of
cyber-security indicators for different vertical sectors,
and supports CTI sharing for both human and ma-
chine applications. More details about MISP func-
tionalities are described in https://github.com/MISP/
MISP.
Intelligence vocabularies (MISP galaxy) can be
bundled with existing threat adversaries, malware
and ransomware or linked to events from MITRE
ATT&CK
2
, which is a publicly available knowledge
base, that contains adversary tactics and techniques
based on real observations. Communities can lever-
age MITRE ATT&CK, in order to develop specific
threat models and methodologies for Tactics, Tech-
niques and Procedures (TTPs).
Finally, MISP provides a flexible free text import
tool to facilitate the integration of unstructured reports
into MISP and an adjustable taxonomy to classify and
tag events according to the users’ own classification
schemes and taxonomies.
The Open Source Intelligence Gathering and Pro-
cessing Framework (GOSINT). GOSINT is another
popular open source platform, developed by Cisco
CSIRT, and it focuses on intelligence gathering and
processing. It collects, processes and exports IoCs; in
this way it controls the data inclusion process in the
platform and enriches it with high-quality metadata.
GOSINT aggregates, validates, and sanitizes indica-
tors for consumption by other tools including MISP
and CRITs
3
, or directly into log management sys-
tems and SIEMs, while also supporting STIX, TAXII
(Trusted Automated eXchange of Intelligence Infor-
mation) and VERIS (Vocabulary for Event Recording
and Incident Sharing) formats used in the CTI sharing
paradigm.
Other than the aforementioned formats, GOSINT
also supports Incident Object Description Exchange
Format (IODEF) and Intrusion Detection Message
Exchange Format (IDMEF), and it allows forensic ex-
perts to gather structured and unstructured data from
incidents occurring at third parties. Thus, it may also
act as a powerful aggregator of IoCs before they are
passed to another analysis platform or a SIEM. Fur-
thermore, GOSINT supports several actions to pro-
vide additional context to indicators in the preprocess-
ing phase. Such actions may include the identification
of IoCs with systems like Cisco Umbrella
4
. The infor-
mation returned from these services may help analysts
judge the value of the indicator, as well as tag the indi-
2
https://attack.mitre.org
3
https://crits.github.io
4
https://umbrella.cisco.com
cator with additional context that might be used later
in the analysis pipeline.
The GOSINT framework is written in Go with
a JavaScript frontend. The main drawbacks of the
GOSINT platform are mainly related to package man-
agement. Specifically, package managers of GOSINT
provide out-of-date versions of the software, and
hence they should be tested to ensure compatibil-
ity. Furthermore, packages’ names are differentiated,
with regard to the package managers or OS release
repository at hand.
Open Threat Partner Exchange (OpenTPX).
OpenTPX
5
is a JSON-based data model repository
platform that enables registering and sharing inci-
dent information. It supports several well-known
protocols including HTTP, SMTP, FTP, and so on,
and it was created to build highly scalable machine-
readable threat intelligence, analysis and network se-
curity products that exchange data at large volumes
and at high speed. Moreover, OpenTPX defines a
comprehensive model for describing cyber-threats,
and also provides mechanisms to convey network
topology information, network ownership, network
segmentation, threat metadata, threat intelligence and
mitigation actions. Furthermore, aspects of data avail-
able in the STIX format (e.g., indicators), have direct
mapping to OpenTPX. Finally, it uses the threat score
conceptual model (LookingGlass, 2015), which aims
to describe, in a comprehensive manner, the score of
the security landscape.
Your Everyday Threat Intelligence (YETI). YETI
is an open source, distributed, machine and analyst-
friendly threat intelligence repository
6
. YETI is a
platform meant to organize observables, IoCs, TTPs,
and threat intelligence in a single, unified repository.
Moreover, YETI automatically enriches observables
(e.g., by resolving domains and geolocating IPs) on
behalf of the user and provides a bootstrap-based user
interface for humans and an API-based for machines
so that to facilitate communication and interoperabil-
ity with other CTI tools
7
.
Trusted Automated Exchange of Indicator Infor-
mation (OpenTAXII). The OpenTAXII platform
8
is
an upgraded form of TAXII services. Its architecture
follows the TAXII specifications with functional units
for the TAXII transfer unit, the TAXII message han-
dler, and other back-end services. OpenTAXII is a
robust Python implementation of TAXII services that
delivers a rich feature set. It offers extendable persis-
tence and authentication layers (both via a dedicated
5
https://opentpx.org/
6
https://yeti-platform.github.io/
7
http://gosint.readthedocs.io/en/latest/
8
http://www.opentaxii.org/en/stable/
The Quest for the Appropriate Cyber-threat Intelligence Sharing Platform
373
API) and provides a collection of threat specifications.
Additionally, it provides an appropriate set of services
and message exchange functionality to facilitate CTI
sharing between parties. Some other characteristics
of OpenTAXII include customizable APIs, authenti-
cation and flexible logging. Furthermore, it automat-
ically handles the data of the frameworks, provides
machine-readable threat intelligence, and combines
network security operations data with threat intelli-
gence, analysis and scoring of data in an optimized
manner.
Collective Intelligence Frameworks (CIF). CIF
9
is
a CTI management system and one of the platforms
of choice of ENISA for CTI sharing (ENISA, 2017).
CIF helps users to parse, normalize, store, post-
process, query, share and produce CTI data, while
allowing them to combine known malicious threat
information from many sources and utilize that in-
formation for identification (incident response), de-
tection (Intrusion Detection System) and mitigation
(null route). It also supports an automated form of
the most common types of threat intelligence such
as IP addresses and URLs that are observed to be
related to malicious activity. The CIF framework
aggregates various data-observations from different
sources. When a user queries for CTI data, the system
returns a series of chronologically ordered messages;
users are then able to make decisions by examining
the returned results (e.g., series of observations about
a particular adversary) in a way similar to examin-
ing an email threat. The CIF server consists of a few
different modules including: CIF-smrt, CIF-worker,
CIF-starman, CIF-router and ElasticSearch.
The CIF-smrt module has two primary capabili-
ties: (a) to fetch files using http(s) and (b) to parse
files using built-in parsers for regular expressions,
JSON, XML, RSS, HTML and plain text files.
Finally, the CIF-worker module helps CIF ex-
tract additional intelligence from collected threat data,
the CIF-starman module offers an HTTP API envi-
ronment, the CIF-router module provides the broker
mechanism between the client and web framework,
while the ElasticSearch module is a data warehouse
for storing (meta)data for intrusions.
4 CTI PLATFORMS EVALUATION
An observed outcome reached out from the character-
istics of the six referred platforms, is the utilization
of the same threat intelligence framework, namely
9
https://github.com/csirtgadgets/massive-octo-spice/wiki/
The-CIF-Book
STIX; notice that STIX and TAXII are currently two
of the most used sources in the threat intelligence plat-
forms.
We will now review the benefits and the draw-
backs of the six CTI sharing platforms we have con-
sidered in Section 3. For convenience, our findings
are summarized in Figure 3. In the following, we
present the highlights of each platform.
MISP: platform is fully organized and the range of
individuals that could utilize it could be developers
or even simple users, providing material for stand-
alone sharing. It is very flexible, expandable and
automated. The information in the database can be
extended by external sources while its functionality
can be extended by integrating with third-party tools.
MISP is both human and machine readable, making
correlations between observables and attributes pos-
sible, which is an exceptional characteristic consisted
by series of data models created by MISP community.
GOSINT: has an organized repository, a managing
system and exporting data functionalities. It can also
be extended by external sources (URL, TEXT, AD-
HOC). It has a community that applies research that
automatically identifies similar, or identical, indica-
tors of malicious behavior. Finally, GOSIT is both
human and machine readable.
OpenTPX: has an organized repository, is very flex-
ible, extensible and provides automation support. It
also offers enhancements of data capabilities by al-
lowing extensions to threat observables descriptions.
It provides a comprehensive threat-scoring frame-
work that allows security analysts, threat researchers,
network security operations and incident responders
to make relevant threat mitigation decisions straight-
forwardly.
YETI Platform: has an organized repository, is very
flexible, extensible and provides automation support.
It is both human and machine readable. YETI’s goal,
is to turn it into a self-sustainable project, where not
only the core developers but the whole community
helps out. To this end, the communication between
community partners is centralized and is based on
GitHub.
OpenTAXII: has an organized repository and manag-
ing system, and can also mimic already known cases
and threats. It is flexible and extendable since it is pro-
viding machine-readable threat intelligence, possibil-
ity of layer extension, source intelligent extension and
APIs extension. It also provides automation support.
CIF: has an organized repository and managing sys-
tem. It also offers data exporting facilities. It pro-
vides combination of malicious threats and utilize that
information for identification (incident response), de-
tection (IDS) and mitigation (null route). CIF can be
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
374
Platform
MISP GOSINT OpenTPX
YETI
OpenTAXII CIF
Organized
repository
MISP GALAXY
(support of big
objects and complex
data), taxonomies,
MITRE ATT&CK
Taxonomies, alert
data, intrusions
Network topology
information, network
ownership, network
segmentation, threat
metadata, threat
intelligence,
mitigation actions,
network security
products
XML feeds, JSON
feeds, taxonomies
References, data
and metadata of
threats, mitigation
actions
CIF intelligence
vocabularies
(CIF-feeds),
Combination of
malicious threats
Organized
community
Gitter community
GOSINT community
(chatting)
Threat scoring
framework
YETI GitHub
Community
- -
Helpful
documentation
Draft documents and
training material
Draft documents,
lexicons, artifacts
- MISP instances - -
Creativity
Already applied and
suggested data
models, opportunity
to make your own
models
-
Comprehensive
model of threat
associated, tools of
optimizing threats
- - -
Added tools Free text import tool - - - - -
Additional
benefits
Organized GitHub,
complete online
documentation
- - - - -
Drawbacks -
Not providing
updates. Package
managers may name
packages differently
It is not human
readable only
machine
Does not provide
tools for creation of
incident attack.
Does not make
correlations
between observable
and attributes
Does not provide
tools for creation of
incident attacks
Only observed
threats (such as IPs)
Figure 3: CTI platform comparison.
The Quest for the Appropriate Cyber-threat Intelligence Sharing Platform
375
Table 1: Overall scoring of the referred platforms.
Platforms MISP GOSINT OpenTPX YETI OpenTAXII CIF
Interoperable 2 2 1 1 2 1
Expressiveness 2 1 2 1 1 1
Flexibility 2 1 2 2 1 1
Extensibility 2 2 1 1 2 1
Automation 2 2 2 2 2 2
Human/machine readable 2 2 - 2 1 2
Overall score 12 10 - 9 9 8
extended by indicators of malicious behavior. It also
provides automation support. Finally, it is both hu-
man and machine readable.
Based on the above discussion, we proceed to map
in a simple manner the extent to which properties of
Requirements 3, 4 and 5 are being met by the candi-
date platforms. Requirement 7 is not considered since
all tools are open source. Score values are analyzed
in the following table:
Score Explanation
- not supported
1 supported to a satisfying level
2 supported to a high level
As shown in Table 1 all platforms support a num-
ber of requirements from those presented in Section 3,
to a different extend as depicted in their score.
From the above comparison, it is evident that
MISP and GOSINT are taking the lead in platforms’
race, when compared to the rest of the platforms.
5 CONCLUSIONS
This paper presented an overview of CTI sharing. Ini-
tially, we have illustrated the corresponding threat in-
formation types (indicators, tactics, alerts, etc.), re-
viewed possible CTI sources, and detailed the CTI
processing and sharing cycle. Then, we have dis-
cussed the benefits and presented the challenges of
CTI sharing. Moreover, we have presented a set of
seven high-level recommendations for a CTI shar-
ing platform that can be used for evaluation. Sub-
sequently, we have surveyed six state-of-the-art CTI
sharing platforms (MISP, GOSINT, OpenTPX, YETI,
OpenTAXII and CIF) and compared and evaluated
them, using the suggested recommendations.
REFERENCES
Brown, S., Gommers, J., and Serrano, O. S. (2015). From
cyber security information sharing to threat manage-
ment. In WISCS 2015, pages 43–49.
Dalziel, H. (2015). How to Define and Build an Effective
Cyber Threat Intelligence Capability. Elsevier.
Dandurand, L. and Serrano, O. S. (2013). Towards im-
proved cyber security information sharing. In CyCon
2013, pages 1–16.
Deloitte (2015). Building an informed community: New
cyber-threat landscape makes sharing intelligence im-
perative. Online. Accessed: 3 Mar. 2019.
ENISA (2013). Detect, share, protect: Solutions for im-
proving threat data exchange among certs. Online.
Accessed: 26 Feb. 2019.
ENISA (2015). Information sharing and common tax-
onomies between csirts and law enforcement. Online.
Accessed: 26 Feb. 2019.
ENISA (2016). Report on cyber security information shar-
ing in the energy sector. Online. Accessed: 26 Feb.
2019.
ENISA (2017). Enisa threat landscape report 2017 - 15 top
cyber-threats and trends. Online. Accessed: 27 Feb.
2019.
ENISA (2018). Information sharing and analysis centres
(isacs): Cooperative models. Online. Accessed: 25
Feb. 2019.
Farnham, G. (2013). Tools and standards for cyber threat
intelligence projects. SANS Institute Information Se-
curity Reading Room.
Friedman, J. and Bouchard, M. (2015). Definitive Guide to
Cyber Threat Intelligence. CyberEdge.
Hernandez-Ardieta, J., Tapiador, J., and Suarez-Tangil, G.
(2013). Information sharing models for cooperative
cyber defense. In CyCon 2013, pages 63–90.
LookingGlass (2015). Opentpx v2.2. Online. Accessed: 3
Mar. 2019.
MISP-Community (2019). Misp user guide: A threat shar-
ing platform. Online. Accessed: 2 Mar. 2019.
NIST (2016). Guide to Cyber Threat Information Sharing.
Special Publication, 800(150).
OASIS-Open (2018). Introduction to stix. Online. Ac-
cessed: 2 Mar. 2019.
Sauerwein, C., Sillaber, C., Mussmann, A., and Breu, R.
(2017). Threat intelligence sharing platforms: An ex-
ploratory study of software vendors and research per-
spectives. In WI 2017.
Skopik, F. (2018). Collaborative cyber threat intelligence:
Detecting and responding to advanced cyber attacks at
national level. CRC Press.
Wagner, C., Dulaunoy, A., Wagener, G., and Iklody, A.
(2016). MISP: the design and implementation of a
collaborative threat intelligence sharing platform. In
WISCS 2016, pages 49–56.
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
376