Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

The staggering development of cyber threats has propelled experts, professionals and specialists in the field security into more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected limiting erroneously simultaneously. Nonetheless, proficiency IDS framework depends essentially on extracted features from network traffic an classifier abnormal or normal traffic. prime impetus this study is to increase performance networks by building a two-phase reinforce subsequently enhance rate diminish false alarm. initial stage utilizes developed algorithm proficient wrapper-approach-based feature selection created multi-objective BAT (MOBBAT). subsequent obtained categorize based newly upgraded (EBAT) training multilayer perceptron (EBATMLP), improve performance. resulting methodology known as (MOB-EBATMLP). efficiency our proposition been assessed utilizing mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, CICIDS2017 established standard datasets evaluating IDS. outcome experimental analysis demonstrates noteworthy advancement above other techniques.