Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
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| Title: | Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics |
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| Authors: | Dif, Meryem Malak, Bouchiha, Mouhamed Amine, Korba, Abdelaziz Amara, Ghamri-Doudane, Yacine |
| Source: | 2025 IEEE 50th Conference on Local Computer Networks (LCN). :1-10 |
| Publication Status: | Preprint |
| Publisher Information: | IEEE, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Machine Learning, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Emerging Technologies (cs.ET), Cryptography and Security, Artificial Intelligence, Networking and Internet Architecture, Cryptography and Security (cs.CR), Emerging Technologies, Machine Learning (cs.LG) |
| Description: | The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust analytics, and interpretable decision support. Specifically, we design an AAI architecture comprising dedicated agents for cyber-threat detection and response at charging stations, real-time State of Charge (SoC) estimation, and State of Health (SoH) anomaly detection, all coordinated through a shared, explainable reasoning layer; develop interpretable threat-mitigation mechanisms that proactively identify and neutralize attacks on both physical charging points and learning components; propose resilient SoC and SoH models that leverage continuous and adversarial-aware learning to produce accurate, uncertainty-aware forecasts with human-readable explanations; and implement a three-agent pipeline, where each agent uses LLM-driven reasoning and dynamic tool invocation to interpret intent, contextualize tasks, and execute formal optimizations for user-centric assistance. Finally, we validate our framework through comprehensive experiments across diverse IoEV scenarios, demonstrating significant improvements in security and prediction accuracy. All datasets, models, and code will be released publicly. 10 pages, 7 figures, Accepted at LCN'25 |
| Document Type: | Article |
| DOI: | 10.1109/lcn65610.2025.11146357 |
| DOI: | 10.48550/arxiv.2509.12233 |
| Access URL: | http://arxiv.org/abs/2509.12233 |
| Rights: | STM Policy #29 CC BY NC ND |
| Accession Number: | edsair.doi.dedup.....04a4c023bfacb857b94a60016ffaa5c2 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust analytics, and interpretable decision support. Specifically, we design an AAI architecture comprising dedicated agents for cyber-threat detection and response at charging stations, real-time State of Charge (SoC) estimation, and State of Health (SoH) anomaly detection, all coordinated through a shared, explainable reasoning layer; develop interpretable threat-mitigation mechanisms that proactively identify and neutralize attacks on both physical charging points and learning components; propose resilient SoC and SoH models that leverage continuous and adversarial-aware learning to produce accurate, uncertainty-aware forecasts with human-readable explanations; and implement a three-agent pipeline, where each agent uses LLM-driven reasoning and dynamic tool invocation to interpret intent, contextualize tasks, and execute formal optimizations for user-centric assistance. Finally, we validate our framework through comprehensive experiments across diverse IoEV scenarios, demonstrating significant improvements in security and prediction accuracy. All datasets, models, and code will be released publicly.<br />10 pages, 7 figures, Accepted at LCN'25 |
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| DOI: | 10.1109/lcn65610.2025.11146357 |