Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems

Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniq...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 22; H. 7; S. 4507 - 4518
Hauptverfasser:	Ashraf, Javed, Bakhshi, Asim D., Moustafa, Nour, Khurshid, Hasnat, Javed, Abdullah, Beheshti, Amin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms autoencoder Autonomous vehicles CAN bus Computer architecture Computer crime Computer networks Cybersecurity Datasets Deep learning Denial of service attacks Gateways Intelligent transport systems Intelligent transportation systems Internet of Vehicles Intrusion detection intrusion detection system Intrusion detection systems LSTM Machine learning Security management Short term Spoofing Training Transportation networks
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
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