Journal Article

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

Kang, Min-Joo, Kang, Je-Won

2016

Overview

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Artificial intelligence / Artificial neural networks / Automobile safety / Belief networks / Biology and Life Sciences / Classification / Communication / Comparative analysis / Computer and Information Sciences / Computer security / Controller area network / Cybersecurity / Data security / Engineering and Technology / Feature extraction / Information processing / Intrusion detection systems / Machine learning / Methods / Models, Theoretical / Motor Vehicles / Neural networks / Neural Networks (Computer) / Parameters / Physical Sciences / Protocol / Security / Sensors / Social Sciences / Time response / Traffic congestion / Traffic flow / Wireless networks