Learning Process Behavioral Baselines for Anomaly Detection
Fawaz, A.M., Sanders, W.H.
Abstract
AbIntrusion resilience is a protection strategy aimed at building systems that can continue to provide service during attacks. One approach to intrusion resilience is to continuously monitor a system’s state and change its configuration to maintain service even while attacks are occurring. Intrusion detection, through both anomaly detection (for unknown attacks) and signature detection (for known attacks) is thus a crucial part of that resilience strategy. In this paper, we introduce KOBRA, an online anomaly detection engine that learns behavioral baselines for applications. KOBRA is implemented as a set of cooperative kernel modules that collects time-stamped process events. The process events are converted to a discrete-time signal in the polar space. We learn local patterns that occur in the data and then learn the normal co-occurrence relationships between the patterns. The patterns and the co-occurrence relations model the normal behavioral baseline of an application. We compute an anomaly score for tested traces and compare it against a threshold for anomaly detection. We evaluate the baseline by experimenting with its ability to discriminate between different processes and detect malicious behavior.
Copyright Notice
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
- The following copyright notice applies to all of the above items that appear in IEEE publications: "Personal use of this material is permitted. However, permission to reprint/publish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE."
- The following copyright notice applies to all of the above items that appear in ACM publications: "© ACM, effective the year of publication shown in the bibliographic information. This file is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the journal or proceedings indicated in the bibliographic data for each item."
- The following copyright notice applies to all of the above items that appear in IFAC publications: "Document is being reproduced under permission of the Copyright Holder. Use or reproduction of the Document is for informational or personal use only."