Intelligent Transportation Systems (ITS), particularly Autonomous Vehicles (AVs), are susceptible to safety and security concerns that impend people's lives. Nothing like manually controlled vehicles, the safekeeping of communications and computing constituents of AVs can be threatened using sophisticated hacking techniques, consequently disrupting AVs from the operative usage in our daily life routines. Once manually controlled vehicles are linked to the Internet, so-called the Internet of Vehicles (IoVs), they would be misused by cyberattacks. In this paper, we present a multi-stage intrusion detection framework to identify intrusions from ITSs and produce low rate of false alarms. The proposed framework can automatically distinguish intrusions in real-time. The proposed framework is based on normal state-based and a deep learning-centered bidirectional Long Short Term Memory (LSTM) architecture to efficiently discover intrusions from the fundamental network gateways and communication networks of AVs. The designed framework is evaluated through two benchmark datasources, that is, the UNSWNB-15 datasource for exterior network communications and the car hacking datasource for in-vehicle communications. The outcomes indicated that the proposed framework achieves high performance that outperforms various current state-of-the-art systems with an accuracy rate of 98.88% for the UNSWNB-15 dataset and 99.11% for the car hacking dataset. Besides, the proposed framework is furthermore capable to detect zero-day (concealed) outbreaks from IoVs networks.
|Number of pages||10|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Early online date||20 Aug 2021|
|Publication status||E-pub ahead of print - 20 Aug 2021|
- autonomous vehicles
- Autonomous vehicles
- Computer crime
- deep learning.
- Intelligent transport systems
- Intrusion detection
- intrusion detection