Title

Deep Learning and Blockchain-based Framework to Detect Malware in Autonomous Vehicles

Document Type

Conference Proceeding

Publication Title

2022 International Wireless Communications and Mobile Computing

Abstract

The advancement in technology has brought to life the concept of Autonomous vehicles (AV). The primary goal of AV is to reduce driving stress and provide comfort to the occupants. Since AVs can drive themselves, it poses a question of passenger security. Furthermore, AVs are connected to an open network like a public Internet to communicate to the outer world, raising security and privacy concerns. Skillful attackers can effortlessly infiltrate the vehicle by injecting malware which can disrupt the regular operation of the entire AV system. A Deep Learning (DL) and Blockchain framework is proposed for AV to resolve the aforementioned security challenges. The network traffic is continuously monitored, and the malware binaries are converted to grey-scale images, which are then classified by Convolutional Neural Network (CNN) employed in the DL model. The CNN architecture, ResNet50V2, has been tested and proves to be efficient in detecting malware with an accuracy of 97.56%. © 2022 IEEE.

First Page

278

Last Page

283

DOI

10.1109/IWCMC55113.2022.9824186

Publication Date

7-19-2022

Keywords

AV, Blockchain, CNN, DL, Malware, ResNet, Security, Convolutional neural networks, Deep learning, Malware, Network security

Comments

IR Deposit conditions: non-described

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