{
  "id": 19461,
  "url": "https://arxiv.org/abs/2608.12431v1",
  "title": "The energetic cost of mitigating AI attacks in cellular networks",
  "summary": "The integration of Artificial Intelligence (AI), generally as Machine Learning (ML) algorithms, in all levels and aspects of cellular networks demonstrates the success of data-driven algorithms; for example, the Radio Intelligence Controller (RIC) of the O-RAN paradigm bestows the network with optimised radio resource allocation, load balancing or energy efficiency functions, among others. Nevertheless, this dependency on data opens new security vulnerabilities, as attackers can alter data prope",
  "authors": "Adrián Losada, Hao Qiang Luo-Chen, David Segura, Carlos S. Alvarez-Merino, Milan Groshev, Emil J. Khatib, Raquel Barco",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T12:42:46.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "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/19461",
  "original_url": "https://arxiv.org/abs/2608.12431v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}