Abstract. We address the semantic gap problem in behavioral monitoring by using hierarchical behavior graphs to infer high-level behaviors from myriad low-level events. Our experimental system traces the execution of a process, performing data-flow analysis to identify meaningful actions such as "proxying", "keystroke logging", "data leaking", and "downloading and executing a program" from complex combinations of rudimentary system calls. To preemptively address evasive malware behavior, our specifications are carefully crafted to detect alternative sequences of events that achieve the same high-level goal. We tested eleven benign programs, variants from seven malicious bot families, four trojans, and three mass-mailing worms and found that we were able to thoroughly identify high-level behaviors across this diverse code base. Moreover, we effectively distinguished malicious execution of high-level behaviors from benign by identifying remotely-initiated actions.
Martignoniet al. REF leveraged hierarchical behavioral graphs to infer high-level behavior of low-level events.
A Layered Architecture for Detecting Malicious Behaviors
In Symposium on Recent Advances in Intrusion Detection (RAID
Computer Science