Quantum Cryptography-Enhanced Cyber Security Intrusion Detection System APTs Attacks in Blockchain:

Senthil G. A. Agni College of Technology, India https://orcid.org/0000-0001-7442-5499 R. Prabha Sri Sairam Institute of Technology, India P. Priyanga RNS Institute of Technology, India S. Sridevi Vels Institute of Science, Technology, and Advanced Studies, India https://orcid.org/0000-0003-2227-4371 Quantum Cryptography-Enhanced Cyber Security Intrusion Detection System APTs Attacks in Blockchain

The novel proposed in this paper aims to revolutionize cybersecurity within Blockchain systems by integrating Quantum Cryptography with federated deep reinforcement learning intrusion detection systems (IDPS). This pioneering fusion of cutting-edge technologies offers a multifaceted defense mechanism against advanced persistent threats (APTs) while preserving the decentralized nature of Blockchain networks. Complementing Quantum Cryptography, federated deep reinforcement learning enhances cybersecurity by deploying AI-driven intrusion detection systems across decentralized Blockchain nodes. This decentralized learning paradigm empowers Blockchain networks to adapt dynamically to evolving cyber threats, ensuring timely and effective responses to malicious activities. Quantum Cryptography and federated deep reinforcement learning, the proposed framework defines strategy against sophisticated cyber-attacks, bolstering the resilience of Blockchain systems. Markov Decision Process is the reinforcement learning algorithm used in the proposed system that detects cyber-attacks and threats.
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