Toward Open-Set Intrusion Detection in VANETs: An Efficient Meta-Recognition Approach

Vehicular intrusion detection systems (IDS) are crucial to ensure the security of vehicular ad hoc networks (VANETs). However, most current IDS for vehicles have been developed using closed datasets, resulting in a limited detection range. Furthermore, in the real world, updates to IDS often fall be...

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Bibliographic Details
Published in:IEEE transactions on network science and engineering 2024-11, Vol.11 (6), p.6589-6604
Main Authors: Zhang, Jing, Pan, Zichen, Cui, Jie, Zhong, Hong, Li, Jiaxin, He, Debiao
Format: Article
Language:English
Subjects:
Accuracy
Aerospace electronics
Extreme value theory
Extreme values
Feature extraction
Intrusion detection
Intrusion detection systems
meta-recognition
Mobile ad hoc networks
Neural networks
Predictions
Security
Training
VANETs
Vehicular ad hoc networks
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Summary:Vehicular intrusion detection systems (IDS) are crucial to ensure the security of vehicular ad hoc networks (VANETs). However, most current IDS for vehicles have been developed using closed datasets, resulting in a limited detection range. Furthermore, in the real world, updates to IDS often fall behind the emergence of novel and unknown attacks, rendering these systems ineffective in defending against such attacks. To overcome this limitation and protect against network attacks in open scenarios, we propose a novel vehicular intrusion detection method that uses meta-recognition. This method utilizes a new neural network to extract joint features and calibrate the predicted values of a pre-trained model via extreme value theory (EVT). In addition, to adapt to the VANETs environment, we introduce temperature scaling and tail separation sampling methods to enhance the modeling effect and increase the prediction accuracy. Comprehensive experiments indicated that the proposed method can detect known attacks at a fine-grained level, identify unknown attacks, and outperform the current state-of-the-art schemes.
ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2024.3459087