Machine Learning Model for Detecting Side-Channel Attacks in Next-Generation 5G Systems

Abstract

The explosive growth of the fifth-generation (5G) mobile networks has reshaped the digital ecosystem in that it has made it possible to achieve ultra-low latency, massive connectivity, and high data rates. Although they are used to advance things like IoT, autonomous vehicles, and smart healthcare, new security challenges are also presented. Side-channel attacks are considered to be one of the greatest threats because they utilize indirect information leakage including power consumption, electromagnetic emissions, timing variations, and cache behavior. Mobile gadgets are particularly risky as they have limited resources, diverse hardware, and constant connectivity. These sneaky attacks are sometimes not easily dealt with by the traditional means of security and this presents the issue of intelligent and adaptable detection systems. The paper suggests a side-channel attack detection model, which is an AI-driven mobile security framework within a 5G setting. The model includes deep learning models like CNNs and LSTMs to process multi-modal side-channel data and identify anomalies. Through edge computing, the framework provides improved scalability, real-time processing at reduced latency. The outcomes of simulation prove that the simulation is highly accurate, has low false-positive, and response time is shorter than traditional methods. Also, explainable AI technologies improve transparency and trust. In general, the presented system will deliver a robust, scalable and efficient system to ensure devices are secured in the next-generation 5G networks.