An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks

We expect an increase in the frequency and severity of cyber-attacks that comes along with the need for efficient security countermeasures. The process of attributing a cyber-attack helps to construct efficient and targeted mitigating and preventive security measures. In this work, we propose an argumentation-based reasoner (ABR) as a proof-of-concept tool that can help a forensics analyst during the analysis of forensic evidence and the attribution process. Given the evidence collected from a cyber-attack, our reasoner can assist the analyst during the investigation process, by helping him/her to analyze the evidence and identify who performed the attack. Furthermore, it suggests to the analyst where to focus further analyses by giving hints of the missing evidence or new investigation paths to follow. ABR is the first automatic reasoner that can combine both technical and social evidence in the analysis of a cyber-attack, and that can also cope with incomplete and conflicting information. To illustrate how ABR can assist in the analysis and attribution of cyber-attacks we have used examples of cyber-attacks and their analyses as reported in publicly available reports and online literature. We do not mean to either agree or disagree with the analyses presented therein or reach attribution conclusions

APT-MMF: An advanced persistent threat actor attribution method based on multimodal and multilevel feature fusion

Threat actor attribution is a crucial defense strategy for combating advanced persistent threats (APTs). Cyber threat intelligence (CTI), which involves analyzing multisource heterogeneous data from APTs, plays an important role in APT actor attribution. The current attribution methods extract features from different CTI perspectives and employ machine learning models to classify CTI reports according to their threat actors. However, these methods usually extract only one kind of feature and ignore heterogeneous information, especially the attributes and relations of indicators of compromise (IOCs), which form the core of CTI. To address these problems, we propose an APT actor attribution method based on multimodal and multilevel feature fusion (APT-MMF). First, we leverage a heterogeneous attributed graph to characterize APT reports and their IOC information. Then, we extract and fuse multimodal features, including attribute type features, natural language text features and topological relationship features, to construct comprehensive node representations. Furthermore, we design multilevel heterogeneous graph attention networks to learn the deep hidden features of APT report nodes; these networks integrate IOC type-level, metapath-based neighbor node-level, and metapath semantic-level attention. Utilizing multisource threat intelligence, we construct a heterogeneous attributed graph dataset for verification purposes. The experimental results show that our method not only outperforms the existing methods but also demonstrates its good interpretability for attribution analysis tasks