A hybrid deep learning-based intrusion detection system for EV and UAV charging stations

Abstract

This paper proposes a novel approach that leverages a hybrid deep learning framework called the Squirrel Search-optimized Attention-Deep Recurrent Neural Network (SS-ADRNN) to optimize the management of charging stations, ensuring efficient resource allocation while safeguarding user data and minimizing operational costs. The SS-ADRNN model incorporates squirrel search optimization, which is inspired by the foraging behaviour of squirrels, to dynamically adjust charging station operations based on environmental conditions and demand patterns. Additionally, attention mechanisms are employed to prioritize relevant input features, enabling the model to focus on critical information during decision-making processes. Deep recurrent neural networks (RNNs) are utilized to capture temporal dependencies in charging station data, allowing for more accurate predictions and adaptive control strategies. Experimental evaluations demonstrate the effectiveness and feasibility of the proposed SS-ADRNN-based approach in real-world scenarios. The results showcase significant improvements in the detection of malicious traffic and cost minimization compared to traditional charging station management methods. Overall, this research contributes to advancing the field of intelligent charging station optimization, offering a robust and adaptable solution for EV and UAV charging infrastructures that prioritize both security and operational efficiency.

Keywords

• Squirrel Search Algorithm
• attention network
• deep recurrent neural network
• electric vehicle
• unmanned aerial vehicle