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The energetic cost of mitigating AI attacks in cellular networks

arXiv Security Archived Aug 14, 2026 ✓ Full text saved

arXiv:2608.12431v1 Announce Type: new Abstract: 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 vu

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    Computer Science > Cryptography and Security [Submitted on 12 Aug 2026] The energetic cost of mitigating AI attacks in cellular networks Adrián Losada (1), Hao Qiang Luo-Chen (2), David Segura (2), Carlos S. Alvarez-Merino (2), Milan Groshev (1), Emil J. Khatib (2), Raquel Barco (2) ((1) The Laude Technology Company S.L. Madrid (Spain), (2) Telecommunication Research Institute (TELMA), Universidad de Málaga, E.T.S. Ingeniería de Telecomunicación, Málaga (Spain)) 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 properties and steer ML models to underperform or degrade. Conversely, the developed mitigation strategies are effective, but they generate a computational load which, in consequence, results in an energy cost generally overlooked, even in the current energy-awareness context. In this work, consumption of a defence technique is characterised, and the challenges raised by the triad of ML accuracy, robustness and energy efficiency are outlined. Comments: 7 pages, 6 figures, submitted to IEEE Communications Magazine Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.12431 [cs.CR]   (or arXiv:2608.12431v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.12431 Focus to learn more Submission history From: Hao Qiang Luo Chen [view email] [v1] Wed, 12 Aug 2026 12:42:46 UTC (686 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Aug 14, 2026
    Archived
    Aug 14, 2026
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