GFCL: A GRU-based Federated Continual Learning Framework against
Adversarial Attacks in IoV
- URL: http://arxiv.org/abs/2204.11010v1
- Date: Sat, 23 Apr 2022 06:56:37 GMT
- Title: GFCL: A GRU-based Federated Continual Learning Framework against
Adversarial Attacks in IoV
- Authors: Anum Talpur and Mohan Gurusamy
- Abstract summary: Deep Reinforcement Learning (DRL) is one of the widely used ML designs in Internet of Vehicles (IoV) applications.
Standard ML security techniques are not effective in DRL where the algorithm learns to solve sequential decision-making through continuous interaction with the environment.
We propose a Gated Recurrent Unit (GRU)-based federated continual learning (GFCL) anomaly detection framework.
- Score: 3.3758186776249923
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The integration of ML in 5G-based Internet of Vehicles (IoV) networks has
enabled intelligent transportation and smart traffic management. Nonetheless,
the security against adversarial attacks is also increasingly becoming a
challenging task. Specifically, Deep Reinforcement Learning (DRL) is one of the
widely used ML designs in IoV applications. The standard ML security techniques
are not effective in DRL where the algorithm learns to solve sequential
decision-making through continuous interaction with the environment, and the
environment is time-varying, dynamic, and mobile. In this paper, we propose a
Gated Recurrent Unit (GRU)-based federated continual learning (GFCL) anomaly
detection framework against adversarial attacks in IoV. The objective is to
present a lightweight and scalable framework that learns and detects the
illegitimate behavior without having a-priori training dataset consisting of
attack samples. We use GRU to predict a future data sequence to analyze and
detect illegitimate behavior from vehicles in a federated learning-based
distributed manner. We investigate the performance of our framework using
real-world vehicle mobility traces. The results demonstrate the effectiveness
of our proposed solution for different performance metrics.
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