An AI-driven solution to prevent adversarial attacks on mobile Vehicle-to-Microgrid services
Bibliographic record
Abstract
With the increasing integration of Artificial Intelligence (AI) in microgrid control systems, there is a risk that malicious actors may exploit vulnerabilities in machine learning algorithms to disrupt power generation and distribution. In this work, we study the potential impacts of adversarial attacks on Vehicle-to-Microgrid (V2M), and discuss potential defensive countermeasures to prevent these risks. Our analysis shows that the decentralized and adaptive nature of microgrids makes them particularly vulnerable to adversarial attacks, and highlights the need for robust security measures to protect against such threats. We propose a framework to detect and prevent adversarial attacks on V2M services using Generative Adversarial Network (GAN) model and a Machine Learning (ML) classifier. We focus on two adversarial attacks, namely inference and evasion attacks. We test our proposed framework under three attack scenarios to ensure the robustness of our solution. As the adversary’s knowledge of a system determines the success of the executed attacks, we study four gray-box cases where the adversary has access to different percentages of the victim’s training dataset. Moreover, we compare our proposed detection method against four benchmark detectors. Furthermore, we evaluate the effectiveness of our proposed method to detect three benchmark evasion attack. Through simulations, we show that all benchmark detectors fail to successfully detect adversarial attacks, particularly when the attacks are intelligently augmented, obtaining an Adversarial Detection Rate (ADR) of up to 60.4%. On the other hand, our proposed framework outperforms the other detectors and achieves an ADR of 92.5%. • A framework to detect and prevent adversarial attacks on V2M services. • Focus on two adversarial attacks, namely inference and evasion attacks. • Simulation study to test the proposed framework under three attack scenarios. • Simulation study to show the detection of adversarial attacks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".