BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach
- Title
- BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach
- Creator
- Maram B.; Mandala J.; Satish A.R.
- Description
- Intrusion detection system (IDS) is a robust model that plays an essential role in dealing with intrusion detection, especially in detecting abnormal anomalies and unknown attacks. The major challenges faced by IDS are the computation time required for analysis, and the exchange of a huge amount of data from one division of the network to another. For the sake of tackling such limitations, this probe proposes a multi-agent enabled IDS for detecting intrusions using the Bat Sea Lion Optimization (BSLnO) algorithm. The proposed strategy consists of five phases, namely pre-processor agent, reducer agent, augmentation agent, classifier agent, and decision agent. Initially, input data is subjected to pre-processor agent, where pre-processing is carried out using data normalization and missing value imputation. Thereafter, the pre-processed result is fed up to the reducer agent, where dimension reduction is carried out using mutual information. The third step is data augmentation in which the dimensionality of data is enhanced. After that, the augmented result is subjected to classifier agent to classify intrusions or malicious activities present in the network based on hybrid deep learning strategies, namely deep maxout network and deep residual network. A developed BSLnO is implemented by incorporating Bat Algorithm (BA) and Sea Lion Optimization (SLnO) algorithm to train the hybrid classifier. The proposed scheme has acquired a higher precision of 0.936, recall of 0.904, and F-measure of 0.920. 2022 John Wiley & Sons Ltd.
- Source
- International Journal of Adaptive Control and Signal Processing, Vol-36, No. 8, pp. 1909-1930.
- Date
- 2022-01-01
- Publisher
- John Wiley and Sons Ltd
- Subject
- deep maxout network; deep residual network; intrusion detection system; multi-agent system; Sea Lion Optimization
- Coverage
- Maram B., Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Himachal Pradesh, Chitkara University, India; Mandala J., Department of Computer Science and Engineering, Christ (Deemed to be) University, Bengaluru, India; Satish A.R., School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, Vijayawada, India
- Rights
- Restricted Access
- Relation
- ISSN: 8906327; CODEN: IACPE
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Maram B.; Mandala J.; Satish A.R., “BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/15002.