Intrusion detection system using deep neural network for in-vehicle network security

Min Joo Kang, Je Won Kang

Research output: Contribution to journalArticlepeer-review

332 Scopus citations

Abstract

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.

Original languageEnglish
Article numbere0155781
JournalPLoS ONE
Volume11
Issue number6
DOIs
StatePublished - 1 Jun 2016

Fingerprint

Dive into the research topics of 'Intrusion detection system using deep neural network for in-vehicle network security'. Together they form a unique fingerprint.

Cite this