An Anomaly Behavior Analysis Methodology for Network Centric Systems

Abstract

Information systems and their services (referred to as cyberspace) are ubiquitous and touch all aspects of our life. With the exponential growth in cyberspace activities, the number and complexity of cyber-attacks have increased significantly due to an increase in the number of applications with vulnerabilities and the number of attackers. Consequently, it becomes extremely critical to develop efficient network Intrusion Detection Systems (IDS) that can mitigate and protect cyberspace resources and services against cyber-attacks. On the other hand, since each network system and application has its own specification as defined in its protocol, it is hard to develop a single IDS which works properly for all network protocols. The keener approach is to design customized detection engines for each protocol and then aggregate the reports from these engines to define the final security state of the system. In this dissertation, we developed a general methodology based on data mining, statistical analysis and protocol semantics to perform anomaly behavior analysis and detection for network-centric systems and their protocols. In our approach, we develop runtime models of protocol's state transitions during a time interval ΔΤ. We consider any n consecutive messages in a session during the time interval ΔΤ as an n-transition pattern called n-gram. By applying statistical analysis over these n-gram patterns we can accurately model the normal behavior of any protocol. Then we use the amount of the deviation from this normal model to quantify the anomaly score of the protocol activities. If this anomaly score is higher than a well-defined threshold the system marks that activity as a malicious activity. To validate our methodology, we have applied it to two different protocols: DNS (Domain Name System) at the application layer and the IEEE 802.11(WiFi) at the data link layer, where we have achieved good detection results (>95%) with low detection errors (<0.1%).