Behavior profiling for robust anomaly detection
Journal
2010 IEEE International Conference on Wireless Communications, Networking and Information Security, WCNIS 2010
Pages
465-471
Date Issued
2010
Author(s)
Abstract
Internet attacks are evolving using evasion techniques such as polymorphism and stealth scanning. Conventional detection systems using signature-based and/or rule-based anomaly detection techniques no longer suffice. It is difficult to predict what form the next malware attack will take and these pose a great challenge to the design of a robust intrusion detection system. We focus on the anomalous behavioral characteristics between attack and victim when they undergo sequences of compromising actions and that are inherent to the classes of vulnerability-exploit attacks. A new approach, Gestalt, is proposed to statefully capture and monitor activities between hosts and progressively assess possible network anomalies by multilevel behavior tracking, cross-level triggering and correlation, and a probabilistic inference model is proposed for intrusion assessment and detection. Such multilevel design provides a collective perspective to reveal more anomalies than individual levels. We show that Gestalt is robust and effective in detecting polymorphic, stealthy variants of known attacks. ©2010 IEEE.
Subjects
Anomaly detection; Attack accessment; Behavioral analysis; Finite state machine; Netwrok service
SDGs
Other Subjects
Anomaly detection; Attack accessment; Behavioral analysis; Finite state machines; Netwrok service; Computer crime; Contour followers; Wireless networks; Wireless telecommunication systems; Intrusion detection
Type
conference paper