Knowing who to watch: Identifying attackers whose actions are hidden within false alarms and background noise

Abstract

Insider attacks are often subtle and slow, or preceded by behavioral indicators such as organizational rule-breaking which provide the potential for early warning of malicious intent; both these cases pose the problem of identifying attacks from limited evidence contained within a large volume of event data collected from multiple sources over a long period. This paper proposes a scalable solution to this problem by maintaining long-term estimates that individuals or nodes are attackers, rather than retaining event data for post-facto analysis. These estimates are then used as triggers for more detailed investigation. We identify essential attributes of event data, allowing the use of a wide range of indicators, and show how to apply Bayesian statistics to maintain incremental estimates without global updating. The paper provides a theoretical account of the process, a worked example, and a discussion of its practical implications. The work includes examples that identify subtle attack behaviour in subverted network nodes, but the process is not network-specific and is capable of integrating evidence from other sources, such as behavioral indicators, document access logs and financial records, in addition to events identified by network monitoring.