This research presents a novel technique termed Multi-Agent Malicious Behaviour Detection. The goal of Multi-Agent Malicious Behaviour Detection is to provide infrastructure to allow for the detection and observation of malicious multi-agent systems in computer network environments. This research explores combinations of machine learning techniques and fuses them with a multi-agent approach to malicious behaviour detection that effectively blends human expertise from network defenders with modern artificial intelligence. Success of the approach depends on the Multi-Agent Malicious Behaviour Detection system's capability to adapt to evolving malicious multi-agent system communications, even as the malicious software agents in network environments vary in their degree of autonomy and intelligence. This thesis research involves the design of this framework, its implementation into a working tool, and its evaluation using network data generated by an enterprise class network appliance to simulate both a standard educational network and an educational network containing malware traffic.