内容紹介

The modern automobile is a complex piece of technology that uses the Controller Area Network (CAN) bus system as a central system for managing the communication between the electronic control units (ECUs). Despite its central importance, the CAN bus system does not support authentication and authorization mechanisms, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways including Denial of Service (DoS), Fuzzing and Spoofing attacks. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this research, we propose a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We generate our own dataset by first extracting attack-free data from our experimental cars and by injecting attacks into the latter and collecting the dataset. We use the dataset for testing and training our model. Our experiment results demonstrate that our classifier is efficient for detecting the CAN bus system attacks.

詳細情報

刊年

2021

形態

電子化映像資料(31分29秒）

シリーズ名

情報科学領域・コロキアム ; 2021年度

注記

講演者所属: 情報科学領域

講演日: 2021年6月7日 3限

講演場所: 情報科学棟中講義室(L2)

標題言語

英語 (eng)

本文言語

英語 (eng)

著者情報

Hossain, Md Delwar