Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

Autor:	Mohamed Abdulnab, Waheed Ali H. M. Ghanem, Abdullah B. Nasser, Mohammad Tubishat, N. A. M. Alduais, Nibras Abdullah, Yousef A. Baker El-Ebiary, Ola A. Al-wesabi
Rok vydání:	2021
Předmět:	business.industry Computer science Network security Feature selection Intrusion detection system Machine learning computer.software_genre Feature (computer vision) Classifier (linguistics) Key (cryptography) Artificial intelligence business Metaheuristic computer Selection (genetic algorithm)
Zdroj:	Communications in Computer and Information Science ISBN: 9789813368347 ACeS
Popis:	Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::83c9c86fa49698a22a356f0b7af6e126 https://doi.org/10.1007/978-981-33-6835-4_26 Zobrazit plný text záznamu