TRINETR: An architecture for collaborative intrusion detection and knowledge-based alert evaluation

Abstract

Current reactive and standalone network security products are not capable of withstanding the onslaught of diversified network threats. As a result, a new security paradigm, where integrated security devices or systems collaborate closely to achieve enhanced protection and provide multi-layer defenses is emerging. In this paper, we present the design of a collaborative architecture for multiple intrusion detection systems to work together to detect real-time network intrusions. The detection is made more efficient and effective by using collaborative intelligent agents, relevant knowledge base and combination of multiple detection sensors. The architecture is composed of three parts: Collaborative Alert Aggregation, Knowledge-based Alert Evaluation and Alert Correlation. The architecture is aimed at reducing the alert overload by correlating results from multiple sensors to generate condensed views, reducing false positives by integrating network and host system information into the evaluation process and correlating events based on logical relations to generate global and synthesized alert report. The architecture is designed as a layer above intrusion detection for post-detection alert analysis and security actions. The first two parts of the architecture have been implemented and the implementation results are presented in this paper.