A Comprehensive Framework for Detection and Mitigation of Side-Channel Attacks in Contemporary Cryptosystems

This paper presents a comprehensive framework for the detection and mitigation of side-channel attacks in contemporary cryptosystems, addressing the growing threat posed by attackers exploiting unintended information leakage such as power consumption, timing variations, electromagnetic emissions, and cache access patterns. The framework integrates advanced monitoring techniques, machine learning–based anomaly detection, and adaptive countermeasures to identify vulnerabilities across both hardware and software implementations of cryptographic algorithms. By combining static analysis, runtime profiling, and real-time threat assessment, the proposed approach enhances the resilience of cryptographic systems against attacks targeting sensitive operations like key generation, encryption, and authentication. The study further evaluates the effectiveness of the framework through experimental analysis on modern cryptographic architectures, demonstrating improved detection accuracy, reduced leakage exposure, and minimal performance overhead. Overall, the framework offers a scalable and practical solution for strengthening security in contemporary digital communication and data protection systems.