AI 中文总结
该研究针对蜂窝网络中AI攻击的缓解策略，表征了防御技术的能耗，指出需应对ML精度、鲁棒性与能效的三角挑战。
AI 中文摘要
人工智能（AI，通常为机器学习（ML）算法）在蜂窝网络各层级及各方面的集成，体现了数据驱动算法的成功；例如，O-RAN范式的无线电智能控制器（RIC）为网络提供优化的无线电资源分配、负载均衡或能效功能等。然而，这种对数据的依赖带来了新的安全漏洞，攻击者可篡改数据属性，引导ML模型性能下降或失效。相反，已开发的缓解策略虽有效，但会产生计算负载，进而导致能量代价，即便在当前注重能效的背景下，这一代价也常被忽视。本研究对防御技术的能耗进行了表征，并概述了ML精度、鲁棒性与能效三者构成的三角关系所带来的挑战。
英文摘要
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.
Comments7 pages, 6 figures, submitted to IEEE Communications Magazine
