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- Title
Attack classification in network intrusion detection system based on optimization strategy and deep learning methodology.
- Authors
Ramu, Ch.Kodanda; Rao, T. Srinivasa; Rao, E. Uma Shankar
- Abstract
In recent times, network-based applications have rapidly grown in the field of information and communication technology(ICT), which enables individuals and organizations to connect and share their sensitive information seamlessly. The security of these network-based applications is imperative to avoid cyber-attacks during the exchange of sensitive information. The identification of anomalies in network events can be highly challenging due to the complex nature of traffic flows. To solve the challenges, network intrusion detection system (NIDS) technology is used; any network can profit from this system because it can monitor traffic and detect any irregularities. The existing NIDS systems driven by machine learning models do not provide sufficient ability to handle heterogeneous data and realize performance degradation while detecting some types of attacks. Therefore, this paper proposes an innovative meta-heuristic optimization and deep learning-based methodology for improving the performance of NIDS systems. Initially, the raw captured traffic data is fed into the pre-processing phase to attain data standardization and data balancing. Further, an extended Pelican Optimization algorithm (Ex-Pel) is employed to select the set of features from the pre-processed data optimally. Finally, the Self-Attention Assisted Weighted Auto Encoder (SAttn_WAE) is executed to detect the attacks precisely through the set of optimal features. The execution of the proposed methodology is carried out in the Python platform, and performance evaluation is done using accuracy, precision, recall, FAR, and F1-score metrics. The proposed model achieved an accuracy of 99.23%, precision of 99.78%, FAR of 0.67%, recall of 99.13%, and F1-score of 99.32%, which is comparatively better than existing models.
- Subjects
MACHINE learning; OPTIMIZATION algorithms; MATHEMATICAL optimization; INFORMATION &; communication technologies; TRAFFIC flow; DEEP learning
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 31, p75533
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-024-18558-5