Biblio

Threat Actor Attribution is the task of identifying an attacker responsible for an attack. This often requires expert analysis and involves a lot of time. There had been attempts to detect a threat actor using machine learning techniques that use information obtained from the analysis of malware samples. These techniques will only be able to identify the attack, and it is trivial to guess the attacker because various attackers may adopt an attack method. A state-of-the-art method performs attribution of threat actors from text reports using Machine Learning and NLP techniques using Threat Intelligence reports. We use the same set of Threat Reports of Advanced Persistent Threats (APT). In this paper, we propose a Deep Learning architecture to attribute Threat actors based on threat reports obtained from various Threat Intelligence sources. Our work uses Neural Networks to perform the task of attribution and show that our method makes the attribution more accurate than other techniques and state-of-the-art methods.

The task of attack attribution, i.e., identifying the entity responsible for an attack, is complicated and usually requires the involvement of an experienced security expert. Prior attempts to automate attack attribution apply various machine learning techniques on features extracted from the malware's code and behavior in order to identify other similar malware whose authors are known. However, the same malware can be reused by multiple actors, and the actor who performed an attack using a malware might differ from the malware's author. Moreover, information collected during an incident may contain many clues about the identity of the attacker in addition to the malware used. In this paper, we propose a method of attack attribution based on textual analysis of threat intelligence reports, using state of the art algorithms and models from the fields of machine learning and natural language processing (NLP). We have developed a new text representation algorithm which captures the context of the words and requires minimal feature engineering. Our approach relies on vector space representation of incident reports derived from a small collection of labeled reports and a large corpus of general security literature. Both datasets have been made available to the research community. Experimental results show that the proposed representation can attribute attacks more accurately than the baselines' representations. In addition, we show how the proposed approach can be used to identify novel previously unseen threat actors and identify similarities between known threat actors.