Designing a Security Metric for EV-Based Load-Altering Attacks in Transmission Systems
Bibliographic record
Abstract
Due to their cyber vulnerabilities, the increasing integration of electric vehicles (EVs) and their related EV supply equipment (EVSE) makes power grids prone to a variety of cyber attacks. Among possible threats, adversaries can observe frequency measurements and alter the consumption of EVs accordingly, creating an EV-based load-altering attack (EV-LAA). On this basis, this article uses the measurements of the transmission grid and information on its cyber layer to derive a security metric that can be used for diagnosis and condition monitoring of the transmission grid’s security state. First, common vulnerabilities in EV ecosystems are analyzed to devise related attack graphs. Afterward, a Markov decision process (MDP) tree is established based on the obtained attack graphs to display the possible attacker’s actions and their detrimental consequences. In this MDP, to calculate the probabilities of adversaries’ success in each branch, a customized common vulnerability scoring system (CVSS) is developed. Furthermore, control input and measurement signals are used to identify the transmission systems’ model. Using this model, the damping ratio, controllability, and observability of low-damping modes, as well as the number of compromised charging stations, can be obtained for calculating the terms of a reward function. The generated MDP tree is resolved by the Epsilon-Greedy Q-learning algorithm to calculate the value of each state in the MDP tree and the related optimal adversarial action. This metric is integrated into a back propagation neural network (BPNN) to provide a security monitoring framework for attacks originating from the EV ecosystem. The security monitoring framework is evaluated on a testbed to demonstrate its usefulness in quantifying the security status in the case of EV-LAAs. This testbed consists of a virtual sphere (vSphere) of an EV ecosystem with the New England 39-bus transmission system simulated in a real-time simulator (RTS).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".