TechRxiv
DCNN_IDS_conf_arxiv.pdf (264.65 kB)
0/0

DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

Download (264.65 kB)
preprint
posted on 21.04.2020 by Sriram Srinivasan, Shashank A, vinayakumar R, Soman KP
In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

History

Email Address of Submitting Author

sri27395ram@gmail.com

ORCID of Submitting Author

0000-0001-6259-2679

Submitting Author's Institution

Amrita Vishwa Vidyapeetham

Submitting Author's Country

India

Licence

Exports

Licence

Exports