Latent Dirichlet Allocation for the Detection of Multi-Stage Attacks.

The rapid shift and increase in remote access to organisation resources have led to a significant increase in the number of attack vectors and attack surfaces, which in turn has motivated the development of newer and more sophisticated cyber-attacks. Such attacks include Multi-Stage Attacks (MSAs). In MSAs, the attack is executed through several stages. Classifying malicious traffic into stages to get more information about the attack life-cycle becomes a challenge. This paper proposes a malicious traffic clustering approach based on Latent Dirichlet Allocation (LDA). LDA is a topic modelling approach used in natural language processing to address similar problems. The proposed approach is unsupervised learning and therefore will be beneficial in scenarios where traffic data is not labeled and analysis needs to be performed. The proposed approach uncovers intrinsic contexts that relate to different categories of attack stages in MSAs. These are vital insights needed across different areas of cybersecurity teams like Incident Response (IR) within the Security Operations Center (SOC), the insights uncovered could have a positive impact in ensuring that attacks are detected at early stages in MSAs. Besides, for IR, these insights help to understand the attack behavioural patterns and lead to reduced time in recovery following an incident. The proposed approach is evaluated on a publicly available MSAs dataset. The performance results are promising as evidenced by over 99% accuracy in identified malicious traffic clusters.