Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT
- Title
- Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT
- Creator
- Narayanasami S.; Sengan S.; Khurram S.; Arslan F.; Murugaiyan S.K.; Rajan R.; Peroumal V.; Dubey A.K.; Srinivasan S.; Sharma D.K.
- Description
- Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Source
- Wireless Personal Communications, Vol-127, No. 2, pp. 1763-1785.
- Date
- 2022-01-01
- Publisher
- Springer
- Subject
- Bat algorithm; Dataset; Intrusion detection system; Optimal features; SVM
- Coverage
- Narayanasami S., Department of Computer Science and Engineering, St. Martins Engineering College, Telangana, Hyderabad, 500100, India; Sengan S., Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tamil Nadu, Tirunelveli, 627152, India; Khurram S., Biology Teacher, Teaching Human Social Biology and Applied Sciences, Roots IVY International Schools, Punjab, Faisalabad, 38000, Pakistan; Arslan F., University of Engineering and Technology, Punjab, Lahore, 39161, Pakistan; Murugaiyan S.K., Department of Information Technology, Sri Sai Ram Engineering College, Tamil Nadu, Chennai, 600044, India; Rajan R., Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Tamil Nadu, Hosur, 635109, India; Peroumal V., School of Electronics Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, 600048, India; Dubey A.K., Department of Computer Science and Engineering, ABES Engineering College, Uttar Pradesh, Ghaziabad, 201009, India; Srinivasan S., Department of Electronics and Communications Engineering, School of Engineering and Technology, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India; Sharma D.K., Department of Mathematics, Jaypee University of Engineering and Technology, Madhya Pradesh, Guna, 473226, India
- Rights
- Restricted Access
- Relation
- ISSN: 9296212; CODEN: WPCOF
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Narayanasami S.; Sengan S.; Khurram S.; Arslan F.; Murugaiyan S.K.; Rajan R.; Peroumal V.; Dubey A.K.; Srinivasan S.; Sharma D.K., “Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/14924.