Publication Type : Conference Paper
Publisher : Springer Singapore
Source : Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation, Springer Singapore, Singapore (2020)
ISBN : 9789811597008
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2020
Abstract : 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.
Cite this Research Publication : S. Sriram, Shashank, A., Vinayakumar, R., and Dr. Soman K. P., “DCNN-IDS: Deep Convolutional Neural Network Based Intrusion Detection System”, in Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation, Singapore, 2020.