Exploiting timed automata based fuzzy controllers for designing adaptive intrusion detection systems

Network intrusion detection systems (NIDSs) are pattern recognition problems that classify network traffic patterns as either ‘normal’ or ‘abnormal’. Precisely, the main aim of intrusion detection is to identify unauthorized use, misuse, and abuse of computers by detecting malicious network activities such as port scans, denial of service or other attempts to crack computer network environments. Even though the incorporation of conventional Soft Computing techniques in NIDSs has yielded to good solutions, the strong dynamism characterizing network intrusion patterns tend to invalidate the usability of existing framework. To tackle this issue, our proposal performs an adaptive supervised learning on a collection of time series that characterizes the network behavior to create a so-called timed automata-based fuzzy controller (TAFC), i.e. an evolvable fuzzy controller whose dynamic features allow to design an advanced network intrusion detection system able to directly deal with computer network dynamism and support networks’ administrators to prevent eventual damages coming from unauthorized network intrusion. As will be shown in experiments, where our approach has been compared with a conventional Mamdani fuzzy controller, the proposed system reduces the detection error and, as consequence, improves the computer network robustness.