An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement

Lateral movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and lateral movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.