Developing a Security Metric for Assessing the Power Grid’s Posture Against Attacks From EV Charging Ecosystem
Bibliographic record
Abstract
Providing reliable and efficient services for EV users necessitates the use of cyber layers on top of physical layers in EV ecosystems. The deployment of such cyber layers, however, makes these ecosystems an appealing target for various cyber-attacks—e.g., data manipulation, malware injection, and intrusions—which are crafted to deteriorate the operation of power distribution networks. On this basis, this paper develops a metric that captures the security posture of EV ecosystems, considering the possible attacks and their associated impacts on distribution grids. First, potential attack graphs are obtained to show the connections between the adversaries’ access points and the consequences of attack vectors. Then, a Markov decision process (MDP) tree is generated, using probabilities of adversaries’ success rates for a specific attack vector and unique reward functions. The developed MDP tree is then resolved by a policy iteration algorithm to calculate the value function of each state, related subsequent adversarial actions from the attackers’ viewpoint, and quantify the security posture of each state. Finally, using the obtained metric, a deep convolutional neural network (CNN) is trained offline to notify the distribution system operators (DSOs) of the security status of EV ecosystems, i.e., secure and alarm situations. DSOs can use the developed security metrics to design consequent corrective actions during critical cyber attacks. To demonstrate the usefulness of the proposed security metric in quantifying the security status of the grid, a cyber-physical testbed is built. This testbed integrates a virtual sphere (vSphere) to simulate the cyber parts of the EV ecosystem as well as a real-time simulator to model two distribution networks, i.e., IEEE 33- and 141-bus, under DSO control center based on IEC 61850. For a distribution network with dynamic sections that can be created using the operation of tie-switches, a supplementary strategy has also been suggested. This strategy is evaluated under the IEEE 69-bus distribution network to calculate the related security metric and update the security monitoring framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".