Optimized Feature Selection for Enhanced Network Attack Detection Using Bat Algorithm

Cyberattacks try to compromise the security objectives (i.e., confidentiality, integrity, and availability) and stop the services that everyone uses. An Intrusion Detection System (IDS) monitors and reveals cyberattack patterns in network flows. Network packets contain several features that can be used in the detection of cyberattacks. These features comprise several irrelevant or redundant ones that lessen the efficiency of detecting cyberattacks and increase false alarms. This paper inspects the features of the UNSW-NB15 dataset. We employ the Bat Algorithm (BA) as a feature selection to identify the strongest features from the UNSW-NB15 dataset that contribute detect cyberattack patterns. The Adaboost and Logistic Regression (LR) classifiers are employed to assess the complexity in terms of accuracy, precision, and recall. The experimental results show that the Adaboost achieved 100% accuracy, precision, and recall. Meanwhile, the LR achieved 99.2% accuracy, 99.9% precision, and 99.91% recall.