{
  "id": 16136,
  "url": "https://arxiv.org/abs/2608.02274v1",
  "title": "A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks",
  "summary": "Electric Vehicle Charging Systems (EVCSs) are increasingly connected with Internet of Things (IoT) devices, which improves charging intelligence but also expands their exposure to cyber-attacks. Intrusion Detection Systems (IDSs) are essential for securing EV charging networks; however, conventional Machine Learning (ML)-based IDSs often rely on manual model design and mainly optimize detection performance without fully considering inference latency and model size. In this paper, a Multi-Objecti",
  "authors": "Li Yang",
  "category": "research",
  "topics": "military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T14:11:38.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16136",
  "original_url": "https://arxiv.org/abs/2608.02274v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}