PPM-InVIDS: Privacy protection model for in-vehicle intrusion detection system based complex-valued neural network

As the rapidly increasing connectedness of modern vehicles, more and more information security incidents targeting Intelligent Connected Vehicle (ICV) are emerging. Some potential attackers inject malicious packets by external interfaces, which infiltrating the controller area network (CAN), thus implement illegal intrusion. Deep learning-based in-vehicle intrusion detection systems (IDS) among anomaly detection technologies have received a lot attention owing to their high efficiency and accuracy. So, this paper focuses on studying the complex value neural network (CVNN) to detect arbitration field (CAN ID) for protecting CAN network. We proposed an encoder, which can extract shallow features via the auto-encoder algorithm, and furthermore present a random phase that rotates the complex-valued domain features to hide the real features. Then the proposed processing model extracts valuable features with an attention mechanism. Injecting anomaly data in the real vehicle to build the CAN dataset, the real-time detection shows that constructed IDS present a high resulting accuracy, achieving 98%. In particular, the attack experiment indicates that our model makes the adversary hardly inferring valuable information. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper focuses on studying the complex value neural network (CVNN) to detect arbitration field (CAN ID) for protecting CAN network. The constructed IDS in real-time detection shows high accuracy, reaching 98%. The attack experiment indicates that the model makes it difficult for adversaries to infer valuable information.