期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 104, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2022.108447
关键词
Automotive security; Controller area network; Convolutional neural network; Intrusion detection; In-vehicle network; Long-short term memory network; Representation learning; Security and privacy
类别
资金
- Deanship of Scientific Research, King Khalid University (KKU) , Kingdom of Saudi Arabia
- [RGP.2/61/43]
Modern vehicles are becoming increasingly connected, raising concerns about security. Conventional security mechanisms are insufficient to protect in-vehicle networks from attacks, necessitating the development of an effective intrusion detection system (IDS). This study presents IDS-IVN, an IDS for in-vehicle networks that utilizes deep learning to extract and classify traffic features for intrusion detection.
Modern vehicles are increasingly getting connected within the vehicles, with other systems, leading to more concerns about security. Controller area network (CAN) has become a de -facto standard for connecting internal vehicles' components. However, it lacks security features. Conventional security mechanisms fail to protect in-vehicle networks from attacks, requiring the development of an effective intrusion detection system (IDS). This work develops an IDS for in -vehicle networks called IDS-IVN based on a compact representation of location invariant and time-variant traffic features using deep learning. The IDS-IVN uses convolutional neural and long-short-term memory networks as encoder/decoder functions of autoencoder networks to extract features from raw data and classify them using latent space representation into intrusive and non-intrusive classes. A benchmark real-time ROAD dataset is used to demonstrate the IDS-IVN's performance compared to the existing methods. IDS-IVN reports 99% accuracy with a 0.32% low false-positive rate for detecting intrusions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据