@inproceedings{730a637893f04897a6d05622bfd15d38,
title = "An intrusion detection system based on machine learning for CAN-Bus",
abstract = "The CAN-Bus is currently the most widely used vehicle bus network technology, but it is designed for needs of vehicle control system, having massive data and lacking of information security mechanisms and means. The Intrusion Detection System (IDS) based on machine learning is an efficient active information security defense method and suitable for massive data processing. We use a machine learning algorithm—Gradient Boosting Decision Tree (GBDT) in IDS for CAN-Bus and propose a new feature based on entropy as the feature construction of GBDT algorithm. In detection performance, the IDS based on GBDT has a high True Positive (TP) rate and a low False Positive (FP) rate.",
keywords = "CAN-Bus, Detection performance, Entropy, GBDT, IDS, Information security, Machine learning",
author = "Daxin Tian and Yuzhou Li and Yunpeng Wang and Xuting Duan and Congyu Wang and Wenyang Wang and Rong Hui and Peng Guo",
note = "Publisher Copyright: {\textcopyright} 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 3rd International Conference on Industrial Networks and Intelligent Systems, INISCOM 2017 ; Conference date: 04-09-2017 Through 04-09-2017",
year = "2018",
doi = "10.1007/978-3-319-74176-5\_25",
language = "英语",
isbn = "9783319741758",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "285--294",
editor = "Yuanfang Chen and Duong, \{Trung Q.\}",
booktitle = "Industrial Networks and Intelligent Systems - 3rd International Conference, INISCOM 2017, Proceedings",
address = "德国",
}