Chaotic-based optimization, based feature selection with shallow neural network technique for effective identification of intrusion detection
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl.24Keywords:
Chaotic optimization, Feature selection, Shallow neural networks, Intrusion detection, cybersecurity.Dimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The work in this paper attempts to deal with intrusion detection using a chaotic-based optimization technique and feature selection + shallow neural networks. The idea of chaotic systems is used to get randomness in the feature selection process, which can enable a shallow neural network to perform better for intrusion detection. Experiments on benchmark datasets reveal the effectiveness of this proposed solution by significant improvements in detection accuracy, false positive reduction at run-time and computational efficiency as compared to conventional methods.Abstract
How to Cite
Downloads
Similar Articles
- M. Prabhu, A. Chandrabose, Optimization based energy aware scheduling in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Suprabha Amit Kshatriya, Arvind R Yadav, Early detection of fire and smoke using motion estimation algorithms utilizing machine learning , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Annalakshmi D., C. Jayanthi, An asymmetric key encryption and decryption model incorporating optimization techniques for enhanced security and efficiency , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Mohamed Azharudheen A, Vijayalakshmi V, Improvement of data analysis and protection using novel privacy-preserving methods for big data application , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Temesgen A. Asfaw, Batch size impact on enset leaf disease detection , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- A. Sathya, M. S. Mythili, MOHCOA: Multi-objective hermit crab optimization algorithm for feature selection in sentiment analysis of Covid-19 Twitter datasets , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Sowmiya M, Banu Rekha B, Malar E, Ensemble classifiers with hybrid feature selection approach for diagnosis of coronary artery disease , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- M. Prabhu, A. Chandrabose, Improving the resource allocation with enhanced learning in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- R. Kalaiselvi, P. Meenakshi Sundaram, Machine learning-based ERA model for detecting Sybil attacks on mobile ad hoc networks , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Chinnadurai U, A. Vinayagam, Energy efficient routing with cluster approach in wireless networks – A literature review , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- S. Hemalatha, N. Vanjulavalli, K. Sujith, R. Surendiran, Effective gorilla troops optimization-based hierarchical clustering with HOP field neural network for intrusion detection , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- V. Seethala Devi, N. Vanjulavalli, K. Sujith, R. Surendiran, A metaheuristic optimisation algorithm-based optimal feature subset strategy that enhances the machine learning algorithm’s classifier performance , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper