In-vehicle network intrusion detection using deep convolutional neural network

By Hyun Min Song, Jiyoung Woo, Huy Kang Kim. Vehicular Communications, 2020

602 citations recorded by the source corpus.

Abstract The implementation of electronics in modern vehicles has resulted in an increase in attacks targeting in-vehicle networks; thus, attack detection models have caught the attention of the automotive industry and its researchers. Vehicle network security is an urgent and significant problem because the malfunctioning of vehicles can directly affect human and road safety. The controller area network (CAN), which is used as a de facto standard for in-vehicle networks, does not have sufficient security features, such as message encryption and sender authentication, to protect the network from cyber-attacks. In this paper, we propose an intrusion detection system (IDS) based on a deep convolutional neural network (DCNN) to protect the CAN bus of the vehicle. The DCNN learns the network traffic patterns and detects malicious traffic without hand-designed features. We designed the DCNN model, which was optimized for the data traffic of the CAN bus, to achieve high detection performance while reducing the unnecessary complexity in the architecture of the Inception-ResNet model. We performed an experimental study using the datasets we built with a real vehicle to evaluate our detection system. The experimental results demonstrate that the proposed IDS has significantly low false negative rates and error rates when compared to the conventional machine-learning algorithms.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/16c1244bb58da1dead5db0ea5cff371e44bbd08e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Li Yang
2. Abdallah Shami
3. Abdallah Moubayed

In Electrical and Electronic Engineering

1. Li Yang
2. Abdallah Shami
3. Abdallah Moubayed

In Signal Processing

1. Li Yang
2. Abdallah Shami
3. Abdallah Moubayed

Citations in the corpus

• MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles (2021, methodology): “Song et al. [18] proposed a deep convolutional neural network (DCNN) method named Reduced Inception-ResNet to detect intra-vehicle attacks and achieve high detection performance on the CAN-intrusion dataset.”