DEVELOPMENT OF AI ENHANCED DARK WEB DETECTION SYSTEM WITH QUANTUM CRYPTOGRAPHY

Abstract

Quantum computing combined with machine learning provides an environment that allows implementing better security measures within that realm. This methodology provides cutting-edge Quantum Machine Learning algorithms that turn out to be useful in information security measures. Quantum computing is a method of computing based on the principles of quantum mechanics, which allows quite incredibly fast computation, way beyond the standard forms previously available, and new solutions in encryption for secure communications and data storage. In information security, quantum machine learning algorithms are trained using quantum computing, where the quantum computer is used in processing enormous datasets to discover trends and flag anomalies that cannot be explained. The paper surveys some of the QML methods, including quantum support vector machines, quantum neural networks, and quantum clustering algorithms, and how they perform better than traditional methods for intrusion detection, malware classification, and mode detection. This paper also addresses issues and prospects related to quantum machine learning, such as quantum cryptography, which involves robust quantum-resistant algorithms and encryption systems in the face of quantum. It finally pointed out quantum machine learning as a new avenue for cyber security in cyberspace where illegal cyber activities are rapidly evolving and quantum technologies are emerging