Quantum-Inspired Probabilistic Framework for Automated Ransomware Detection

The escalating sophistication of cyber threats calls for innovative approaches to cybersecurity. Traditional ransomware detection methods often struggle to keep pace with the rapid evolution of malicious software. In response, a novel quantum-inspired probabilistic framework has been developed to enhance detection capabilities. This framework leverages principles from quantum mechanics to model the probabilistic behavior of ransomware, enabling more accurate identification of threats. Comprehensive evaluations demonstrate the framework's superior performance in detection accuracy and resource efficiency compared to existing methods. Notably, it exhibits resilience against advanced evasion techniques employed by modern ransomware variants. The integration of quantuminspired methodologies into cybersecurity practices represents a significant advancement in the field. This approach offers a robust solution to the challenges posed by increasingly sophisticated cyber threats. The findings demonstrate the potential of interdisciplinary strategies in enhancing system defenses. The adoption of such frameworks could lead to more effective and proactive cybersecurity measures. Future research may explore the application of quantum-inspired models to other forms of malware. The study contributes to the ongoing efforts to develop advanced tools for cyber threat detection and mitigation.