PDA-CS: Profile Distance Assessment-Centric Cuckoo Search for Anomaly-Based Intrusion Detection in High-Speed Networks

The act of network intrusion detection is an obligatory part of network performance under security. Unlike other network security strategies, the act of intrusion detection systems should aware the behavior of the users and signature of the intruded and normal transactions, which is continuous process since the user behavior is not static as well the attack strategies are redefining in magnified speed. Hence, the objective of effective intrusion detection is always a significant factor for research. The bioinspired evolutionary strategies are getting the attention of most of the recent research studies. In order to this, the divergent contexts such as minimal computational complexity, prediction accuracy, ensemble models have been considered as significant objective. The other most significant objective and compatible to current state of art is IDS scalability and robustness in high-speed networks, hence the evolutionary computation approaches are adoptable. In this study, we propose an intrusion detection approach that is based on evolutionary computation technique called Cuckoo search. Further, the proposed detection system is investigated thoroughly in the context of accuracy, robustness, and also from the evolutionary computation point of view.