Predictive Malicious Identification and Avoidance Using Real-Time Quantum-Enhanced AI

Innovative ways to cyber security are necessary due to the swift evolution of cyber threats, especially ransom ware. Conventional malware detection systems, which mostly use signature-based techniques, are ineffective against complex and dynamic threats. A quantum-enhanced AI framework for real-time malware detection and prevention is proposed in this paper. The framework improves threat detection accuracy and response times by detecting intricate patterns and correlations in network traffic data by utilizing the enormous processing capacity of quantum computing and the versatility of artificial intelligence. According to preliminary findings, the hybrid quantum-classical methodology outperforms conventional techniques by a considerable margin, opening the door for reliable and expandable cybersecurity solutions.