Online Vehicle Forensics Method of Responsible Party for Accidents Based on LSTM-BiDBN External Intrusion Detection  

作　　者：LIU Wen XU Jianxin YANG Genke CHEN Yuanfang 刘文;许剑新;杨根科;陈媛芳

机构地区：[1]Ningbo Industrial Internet Institute,Ningbo 315000,Zhejiang,China [2]Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University,Ningbo 315000,Zhejiang,China [3]Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China [4]College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China [5]School of Cyberspace,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2024年第6期1161-1168,共8页上海交通大学学报(英文版)

基　　金：the National Key R&D Program of China(No.2017YFA60700602)。

摘　　要：Vehicle data is one of the important sources of traffic accident digital forensics.We propose a novel method using long short-term memory-deep belief network by binary encoding(LSTM-BiDBN)controller area network identifier(CAN ID)to extract the event sequence of CAN IDs and the semantic of CAN IDs themselves.Instead of detecting attacks only aimed at a specific CAN ID,the proposed method fully considers the potential interaction between electronic control units.By this means,we can detect whether the vehicle has been invaded by the outside,to online determine the responsible party of the accident.We use our LSTM-BiDBN to distinguish attack-free and abnormal situations on CAN-intrusion-dataset.Experimental results show that our proposed method is more effective in identifying anomalies caused by denial of service attack,fuzzy attack and impersonation attack with an accuracy value of 97.02%,a false-positive rate of 6.09%,and a false-negative rate of 1.94%compared with traditional methods.

关 键 词：digital forensics deep belief network(DBN) long short-term memory(LSTM) binary encoding controller area network identifier(CAN ID) responsible party 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]