AI-Based Adaptive Cybersecurity Model to Anticipate Attacks in the Quantum Era

Abstract

The rapid advancement of quantum computing presents emerging challenges to conventional cybersecurity systems, particularly cryptographic mechanisms and threat detection approaches that rely on traditional computing capabilities. The ability of quantum computers to process complex computations may increase the risk of sophisticated cyberattacks and compromise existing security mechanisms. This study proposes an AI-Based Adaptive Cybersecurity Model designed to anticipate and mitigate cyber threats in the quantum era. The proposed model integrates artificial intelligence techniques, including machine learning and anomaly detection, to identify abnormal patterns, predict potential attacks, and dynamically adapt security responses according to evolving threat characteristics. The research employs a systematic approach consisting of threat identification, data preprocessing, feature extraction, AI model development, attack classification, and adaptive response evaluation. The model is evaluated using cybersecurity datasets containing various types of network attacks, with performance measured using accuracy, precision, recall, F1-score, and detection time. The proposed approach is expected to improve the ability of cybersecurity systems to recognize previously unseen attack patterns and provide proactive responses to emerging threats associated with quantum computing. Furthermore, the integration of AI with adaptive security mechanisms can support continuous threat monitoring and enhance cyber resilience. The findings are expected to contribute to the development of intelligent cybersecurity architectures capable of addressing evolving threats and supporting the transition toward quantum-resistant security infrastructures.