Autonomously Deciding Countermeasures against Threats in Electronic Warfare Settings

This paper investigates the autonomous decision-making process of threat detection, classification, and the selection of alternative countermeasures against threats in electronic warfare settings. We introduce a threat model, which represents a specific threat pattern, and a methodology that compiles the threat into a set of rules using machine learning algorithms. This methodology based upon the inductive threat model could be used to classify real-time threats. Further, we calculate the expected utilities of countermeasures which are applicable given a situation, and provide an intelligent command and control agent with the best countermeasure to threats. We present empirical results that demonstrate the agentpsilas capabilities of classifying threats and choosing countermeasures to them in simulated electronic warfare settings.