Problem
Research questions and friction points this paper is trying to address.
AI security
energy cost
cellular networks
machine learning robustness
defense overhead
The energetic cost of mitigating AI attacks in cellular networks
🤖 AI Summary
This study addresses the critical yet overlooked energy overhead incurred by adversarial defense mechanisms in AI-driven cellular networks. While existing defenses enhance model robustness against adversarial attacks, they introduce substantial energy costs that remain unquantified. This work presents the first systematic characterization of the energy consumption of AI defense strategies within the O-RAN architecture. By simulating adversarial attacks and deploying representative defenses, the authors construct a trade-off model among accuracy, robustness, and energy efficiency. The findings reveal an inherent tension among security, performance, and energy efficiency, offering both theoretical insights and empirical evidence to guide the design of lightweight, energy-efficient AI security mechanisms for next-generation wireless systems.
📝 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 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI security
energy cost
cellular networks
machine learning robustness
O-RAN
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