Role-based lateral movement detection with unsupervised learning

Adversarial lateral movement via compromised accounts remains difficult to discover via traditional rule-based defenses because it generally lacks explicit indicators of compromise. We propose a behavior-based, unsupervised framework comprising two methods of lateral movement detection on enterprise networks: one aimed at generic lateral movement via either exploit or authenticated connections, and one targeting the specific techniques of process injection and hijacking. The first method is based on the premise that the role of a system---the functions it performs on the network---determines the roles of the systems it should make connections with. The adversary meanwhile might move between any systems whatever, possibly seeking out systems with unusual roles that facilitate certain accesses. We use unsupervised learning to cluster systems according to role and identify connections to systems with novel roles as potentially malicious. The second method is based on the premise that the temporal patterns of inter-system processes that facilitate these connections depend on the roles of the systems involved. If a process is compromised by an attacker, these normal patterns might be disrupted in discernible ways. We apply frequent-itemset mining to process sequences to establish regular patterns of communication between systems based on role, and identify rare process sequences as signalling potentially malicious connections.