Future of Information and Communication Conference (FICC) 2025
28-29 April 2025
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: Digital systems in the connected world of today bring convenience but also complicated cyber security challenges. The inadequacies of conventional intrusion detection techniques are exposed by the constant adaptation and exploitation of vulnerabilities by advanced cyber threats. Identifying dangers in massive data flows gets more difficult as networks grow, necessitating innovative methods. With the aim of minimizing these concerns, a new ID model is created utilizing cutting-edge machine learning to proactively and flexibly combat dynamic cyber attacks, with regard to evolving cyber attackers, this model seeks to improve accuracy and protection systems. This research develops an arachnid swarm optimization-based Convolutional neural network (ASO opt CNN) model to improve ID performance. An improved modified residual CNN is employed in the model to lessen the vanishing and exploding gradient problems in deep networks and facilitates the optimization process, making it easier for deep networks to learn. The developed model is adjusted using arachnid swarm optimization (ASO), which is the hybridization particle swarm optimization (PSO) and social spider optimization (SSO). Utilizing test data, the model's efficacy is evaluated at last. This test data is also subjected to preprocessing, which leads to the creation of a robust detection model that can identify the presence of network attacks. Experimentation and comparison indicate the approach's effectiveness by attaining accuracies of 95.95%, 95.61%, and 95.00% for three datasets respectively. This highlights the developed model’s potential to detect intrusions more effectively.
Nishit Patil and Shubhlaxmi Joshi, “Enhanced Arachnid Swarm-Tuned Convolutional Neural Network Model for Efficient Intrusion Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01505117
@article{Patil2024,
title = {Enhanced Arachnid Swarm-Tuned Convolutional Neural Network Model for Efficient Intrusion Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01505117},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01505117},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Nishit Patil and Shubhlaxmi Joshi}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.