A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer

• Published in: Expert systems with applications Vol. 148; p. 113249
• Main Authors: Alazzam, Hadeel, Sharieh, Ahmad, Sabri, Khair Eddin
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.06.2020; Elsevier BV
• Subjects: Algorithms; Feature selection; Intrusion Detection System; Intrusion detection systems; KDDCUP; Machine learning; Model accuracy; Pigeon Inspired Optimizer; 99-00; Feature selection; KDDCUP; 00-01; Intrusion Detection System; Pigeon Inspired Optimizer
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113249

•A summary of feature selection algorithms for intrusion detection system.•A feature selection pigeon optimizer algorithm for intrusion detection system.•A new way to binarize a continuous meta-heuristic algorithm for a discrete problem. Feature selection plays a vital role in building machine learning models. Irrelevant features in data affect the accuracy of the model and increase the training time needed to build the model. Feature selection is an important process to build Intrusion Detection System (IDS). In this paper, a wrapper feature selection algorithm for IDS is proposed. This algorithm uses the pigeon inspired optimizer to utilize the selection process. A new method to binarize a continuous pigeon inspired optimizer is proposed and compared to the traditional way for binarizing continuous swarm intelligent algorithms. The proposed algorithm was evaluated using three popular datasets: KDDCUP99, NLS-KDD and UNSW-NB15. The proposed algorithm outperformed several feature selection algorithms from state-of-the-art related works in terms of TPR, FPR, accuracy, and F-score. Also, the proposed cosine similarity method for binarizing the algorithm has a faster convergence than the sigmoid method.