Anomaly Detection and Mitigation for Electric Vehicle Charging-Based Attacks on the Power Grid
With the increasing adoption of Electric Vehicles (EVs), power grids have to deal with the resulting increase in EV charging loads. A generic method of handling EV loads is load balancing, which requires cooperation from the involved systems, i.e., EVs and Charge Points (CPs). However, if we consider the potential of compromised EVs/CPs, existing load balancing methods fail and the threat of EV charging-based attacks on grid stability arises. In this paper, we address this issue by proposing a combined concept for the detection and mitigation of related attacks. Specifically, we propose a two-step Intrusion Detection System (IDS) that first detects attacks with a potential impact on the grid and in a second step identifies the systems involved in an attack. The design of the IDS enables two attack type-dependent mitigation methods that either correct manipulated data or counteract malicious changes in charging load. Our evaluation identifies specific design choices that enable a good attack detection performance. Additionally, our evaluation shows the effectiveness of the proposed mitigation methods and their relation to IDS performance.
Citation: Dustin Kern, Christoph Krauß and Matthias Hollick. 2025. Anomaly Detection and Mitigation for Electric Vehicle Charging-Based Attacks on the Power Grid. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC). Association for Computing Machinery, to appear
Improving Anomaly Detection for Electric Vehicle Charging with Generative Adversarial Networks
Intrusion Detection Systems (IDSs) are often considered to be an important security mechanism for different use-cases. The Electric Vehicle (EV) charging use-case is one example, with various research articles proposing IDS solutions. One issue in this context, however, is the lack of representative datasets with a variety of realistic attack scenarios, which are vital for evaluating IDSs. Especially concerning the cyber-physical aspects of EV charging, representative datasets are missing and related work usually relieson normal charging data with generating random anomalies or manual attack insertions. This can result in unrealistic or biased attack data. In this paper, we address this issue by proposing a Generative Adversarial Network (GAN)-based IDS training method for EV charging. For this, a GAN is used against a pre-trained IDS to generate attack data that avoids detection. Afterwards, the IDS can be re-trained under consideration of the new attack data in order to eliminate persistent gaps or biases in detection. We implement and evaluate the GAN-based training system. Our evaluation shows the ability of the GAN to identify flaws in existing IDSs. Additionally, we show the effectiveness of re-training IDSs with the GAN output.
Citation: Viet Ha The, Dustin Kern, Phat Nguyen Tan and Christoph Krauß. Improving Anomaly Detection for Electric Vehicle Charging with Generative Adversarial Networks. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC). Association for Computing Machinery, to appear
Data Distribution and Redistribution - A formal and practical Analysis of the DDS Security Standard
The Data Distribution Service (DDS) is a popular communication middleware for the Internet of Things (IoT), providing its own security mechanisms specified in the DDS Security standard. In this work, we formally analyze the authentication handshake protocol and the encryption algorithm used in DDS. We discover a replay vulnerability in the encryption algorithm, implement a proof-ofconcept attack on an open-source implementation of DDS, and review security-relevant changes in the recently published version 1.2.
Citation: Timm Lauser, Maximilian Müller, Ingmar Baumgart and Christoph Krauß. Data Distribution and Redistribution - A formal and practical Analysis of the DDS Security Standard. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC). Association for Computing Machinery, to appear