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Instrumenting Open CTI with a Capability for Attack Attribution Support

Law Enforcement Agencies (LEAs), forensic institutes, national cybersecurity centres and Computer Emergency Response Teams (CERTs), and companies providing cybersecurity services routinely have to investigate cyberattacks on organisations and citizens. In many cases, a key question in such investigations is who is responsible for conducting a given cyberattack. This identification of the source of a cyberattack – which can be a nation state, a crime syndicate, other nefarious group, or even an individual cybercriminal – is often referred to as ‘cyberattack attribution’. In this article, the focus is on technical attack attribution, which is based on the analysis of technical attack traces and Cyber Threat Intelligence (CTI). While it was pointed in [1] that “… questions of responsibility are rarely decided solely through a single technological tool or form of evidence …” [1] (p. 382) and “… a legal approach, rather than a technological one, can solve the attribution problem.” [1] (p. 376), technical attribution is nearly always an indispensable element of any attribution efforts, providing key facts and hypotheses.

Knowing the threat actor behind a cyberattack can be very important and valuable, though the attribution value and investigation priorities vary and depend significantly on the context. For internal cybersecurity teams, CERTs and commercial service providers, attribution efforts usually help understand the attacker’s intentions, capabilities and level of sophistication, modi operandi, and expected behaviour, informing the defenders’ security procedures from prevention to response and remediation and giving them greater confidence. For example, the understanding of the attacker’s tactics, techniques, and procedures (TTPs) guides the defenders in what additional attack traces and artefacts they should look for and what vulnerabilities they have to prioritise for minimising the impact of the ongoing attack and the risk of future ones. In the context of cyberattacks driven by political, military or industrial competition reasons, the attribution (e.g., to a nation state) value can include a reliable view of the impact of sensitive information loss and can extend to driving foreign policy measures. Also, importantly for LEAs, the insights brought by attribution efforts can be instrumental in identifying and prosecuting attackers.

With all the potential benefits, technical analysis involved in cyberattack attribution requires high skills, experience, access to up-to-date CTI, and significant investigators’ effort. Furthermore, attribution results are not always reliable, and skilful attackers often work hard to cover their traces and mislead or confuse investigators. Recognising the challenges, the EU-funded CC-DRIVER [2] and CYBERSPACE [3] projects contributed to designing and developing a tool supporting cyberattack attribution. This article presents the tool and discusses the results of its application in the investigation of a recent cyberattack. We first briefly review several noteworthy challenges of technical attack attribution, the data used in attack analysis, the connections between attribution and other key questions that arise in digital forensics and cyber incident response activities, and the earlier work on applying machine learning to the attack attribution problem. We then explain the technical approach, present the tool, based on a machine learning model and implemented as an extension of the OpenCTI platform [4], and show its performance in the ‘No Pineapple!’ cyberattack investigation carried out by one of the CC-DRIVER and CYBERSPACE partners – WithSecure Corporation. The article is concluded by discussing the challenges and directions for future work.


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