A Game-Theoretic Approach for Alert Prioritization

The quantity of information that is collected and stored in computer systems continues to grow rapidly. At the same time, the sensitivity of such information (e.g., detailed medical records) often makes such information valuable to both external attackers, who may obtain information by compromising a system, and malicious insiders, who may misuse information by exercising their authorization. To mitigate compromises and deter misuse, the security administrators of these resources often deploy various types of intrusion and misuse detection systems, which provide alerts of suspicious events that are worthy of follow-up review. However, in practice, these systems may generate a large number of false alerts, wasting the time of investigators. Given that security administrators have limited budget for investigating alerts, they must prioritize certain types of alerts over others. An important challenge in alert prioritization is that adversaries may take advantage of such behavior to evade detection — specifically by mounting attacks that trigger alerts that are less likely to be investigated. In this paper, we model alert prioritization with adaptive adversaries using a Stackelberg game and introduce an approach to compute the optimal prioritization of alert types. We evaluate our approach using both synthetic data and a real-world dataset of alerts generated from the audit logs of an electronic medical record system in use at a large academic medical center.