Welcome to the InfoSci Platform
Reference Hub1
Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks

Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks

P. Chellammal, Sheba Kezia Malarchelvi, K. Reka, G. Raja
Copyright: © 2022 |Volume: 19 |Issue: 1 |Pages: 16
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781799893462|DOI: 10.4018/IJWSR.310057
Cite Article Cite Article

MLA

Chellammal, P., et al. "Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks." IJWSR vol.19, no.1 2022: pp.1-16. http://doi.org/10.4018/IJWSR.310057

APA

Chellammal, P., Malarchelvi, S. K., Reka, K., & Raja, G. (2022). Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks. International Journal of Web Services Research (IJWSR), 19(1), 1-16. http://doi.org/10.4018/IJWSR.310057

Chicago

Chellammal, P., et al. "Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks," International Journal of Web Services Research (IJWSR) 19, no.1: 1-16. http://doi.org/10.4018/IJWSR.310057

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The process of intrusion detection usually involves identifying complex intrusion signatures from a huge repository. This requires a complex model that can identify these signatures. This work presents a deep learning based neural network model that can perform effective intrusion detection on network transmission data. The proposed multi-layered deep learning network is composed of multiple hidden processing layers in the network that makes it a deep learning network. Detection using the deep network was observed to exhibit effective performances in detecting the intrusion signatures. Experiments were performed on standard benchmark datasets like KDD CUP 99, NSL-KDD, and Koyoto 2006+ datasets. Comparisons were performed with state-of-the-art models in literature, and the results and comparisons indicate high performances by the proposed algorithm.