FuGeIDS: Fuzzy Genetic paradigms in Intrusion Detection Systems
This work addresses the problem of enhancing security against threats for researchers and practitioners in cybersecurity, but it is incremental as it reviews existing methods without introducing novel techniques.
The paper provides an overview of Intrusion Detection Systems (IDS) and examines the application of Genetic Algorithms and Fuzzy Logic for intrusion detection, without presenting new experimental results or specific numerical outcomes.
With the increase in the number of security threats, Intrusion Detection Systems have evolved as a significant countermeasure against these threats. And as such, the topic of Intrusion Detection Systems has become one of the most prominent research topics in recent years. This paper gives an overview of the Intrusion Detection System and looks at two major machine learning paradigms used in Intrusion Detection System, Genetic Algorithms and Fuzzy Logic and how to apply them for intrusion detection.