Contents Intro.

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.

Details

Publication year

2021

Form

電子化映像資料(31分29秒）

Series title

情報科学領域・コロキアム ; 2021年度

Note

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)

Country of publication

Japan

Title language

English (eng)

Language of texts

English (eng)

Author information

Hossain, Md Delwar