Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning

TL;DR

This paper investigates cybersecurity vulnerabilities in 5G-enabled electric vehicle charging stations, analyzing attack impacts and proposing a deep learning-based intrusion detection system with high accuracy.

Contribution

It introduces a simulation of FDI and DDoS attacks on 5G EVCS and proposes a novel LSTM-based IDS using electrical fingerprints for stealthy attack detection.

Findings

Attacks cause oscillations and shifts in EVCS operation.

Detection accuracy of the proposed IDS approaches 100%.

System resiliency depends on attack intensity and target controller.

Abstract

The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G suffers from inherent protocols, hardware, and software vulnerabilities that seriously threaten the communicating entities' cyber-physical security. To overcome these limitations in the EVCS system, this paper analyses the impact of False Data Injection (FDI) and Distributed Denial of Services (DDoS) attacks on the operation of EVCS. This work is an extension of our previously published conference paper about the EVCS. As new features, this paper simulates the FDI attack and the syn flood DDoS attacks on 5G enabled remote Supervisory Control and Data Acquisition (SCADA) system that controls the solar photovoltaics (PV) controller, Battery Energy Storage (BES) controller, and EV controller of the EVCS. The attacks make the EVCS system oscillate or shift the DC operating point. The frequency of oscillation, its damping, and the system's resiliency are found to be related to the attacks' intensity and target controller. Finally, we propose the novel stacked Long Short-Term Memory (LSTM) based intrusion detection systems (IDS) solely based on the electrical fingerprint. This model can detect the stealthy cyberattacks that bypass the cyber layer and go unnoticed in the monitoring system with nearly 100% detection accuracy.