Deep Active Learning Intrusion Detection and Load Balancing in Software-Defined Vehicular Networks

Software-defined vehicular networks (SDVN) can help analyze and reconfigure networks. Massive data generation in autonomous vehicles can lead to issues in network configuration, routing, network characteristics, and system load factors. Load balancing in vehicle sensors helps reduce delays and impro...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-01, Vol.24 (1), p.953-961
Main Authors: Ahmed, Usman, Lin, Jerry Chun-Wei, Srivastava, Gautam, Yun, Unil, Singh, Amit Kumar
Format: Article
Language:English
Subjects:
Active learning
Algorithms
Cybersecurity
Data centers
intelligent load balancing
Intrusion detection systems
Learning
Load balancing
Load management
Load modeling
network performance
Networks
Optimization
Resource management
Resource utilization
road traffic management
Sensors
Servers
smart city application
Software
Software-defined networking
Software-defined networking (SDN)
Task analysis
Vehicles
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Summary:Software-defined vehicular networks (SDVN) can help analyze and reconfigure networks. Massive data generation in autonomous vehicles can lead to issues in network configuration, routing, network characteristics, and system load factors. Load balancing in vehicle sensors helps reduce delays and improve resource utilization. In this paper, we propose a load balancing algorithm to map sensor data, vehicles and data centers performing tasks. A dynamic convergence method is proposed to help identify vehicle system load factors and compare their termination criteria. We also propose a packet-level intrusion detection model. After all load balancing, the model can track the attack on the network. The proposed model further combines the entropy-based active learning and the attention-based model to efficiently identify the attacks. Experiments are then conducted on the standard KDD data to validate the developed models with and without an attention-based active learning mechanism. Our experimental results show that the load balancing mechanism is able to achieve more performance gains than previous techniques. Moreover, the results show that the developed model can improve the decision boundary by using a pooling strategy and an entropy uncertainty measure.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3166864