International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 174 - Number 23 |
Year of Publication: 2021 |
Authors: Jyoti Khurana, Vachali Aggarwal, Harjinder Singh |
10.5120/ijca2021921135 |
Jyoti Khurana, Vachali Aggarwal, Harjinder Singh . A Comparative Study of Deep Learning Models for Network Intrusion Detection. International Journal of Computer Applications. 174, 23 ( Mar 2021), 38-46. DOI=10.5120/ijca2021921135
With the advancement of digital technologies, cybersecurity is attracting more attention as cyber-attacks are becoming more frequent and threatening. A marked upturn has been noticed in the volume and creativity of hacks and cyberattacks. Artificial Intelligence (AI) and Deep Learning (DL) can help address these concerns by contributing to threat detection. They can recognize patterns in data, enabling security systems to learn from former experience. This paper concerns the comparative evaluation of the several techniques of deep learning employed for network intrusion detection.