eUF: A framework for detecting over-the-air malicious updates in autonomous vehicles

Software updates are highly significant in autonomous vehicles. These updates are utilized to provide enhanced features and updated security mechanisms. In order to ensure scalability and smooth roll-out Over-the-air (OTA) mechanism is a preferred option to propagate a software update. However, this approach is vulnerable to security attacks because of existence of wireless communication channel between the vehicle and the manufacturer. In that, an attacker can replace the legitimate software with a malicious software with an intent to get control over the vehicle. In this work, we are motivated to address this problem. We develop an enhanced uptane framework for detection of malicious OTA soft-ware updates in autonomous vehicles. For enhancing security, we incorporate convolutional neural net-work (CNN) in the uptane framework. The proposed framework is able to distinguish between malicious and benign software executables with high accuracy. For training and testing, we create two datasets by collecting executables of Windows and Linux operating system. We encourage the use of transfer learn-ing by exploiting the developed CNN models in order to detect malicious executable designed for auton-omous vehicles. We also benchmark the CNN models against state-of-the art models. Our work is highly beneficial for the community in providing a secure mechanism for software updates.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This article discusses the importance of software updates in autonomous vehicles and proposes an enhanced uptane framework for detecting malicious OTA software updates. By incorporating convolutional neural networks, the framework improves security and accuracy. The authors create two datasets by collecting executables of Windows and Linux operating systems and encourage the use of transfer learning to detect malicious executable files. Finally, the authors benchmark the CNN models and emphasize the significance of this work in providing a secure mechanism for software updates to the community.