Hybrid Quantum Teaching With Adaptive Group Termite Alate Optimization Based Optimal Mixed Block Withholding Attacks

Abstract

The weaknesses inherent in cryptographic exchanges create opportunities for oppositional attacks, compelling attackers to strategically adjust their behaviors for maximum rewards. Moreover, block-withholding attacks have the potential to surpass safeguards when combined with other strategic behaviors, like fork-after-withholding attacks and power adaptive withholding attacks. Recognizing the need for a solution, this research introduces the Hybrid Quantum deep recurrent reinforcement Learning with Adaptive Group teaching Termite alate Optimization (HQL-AGTO) approach in a resource management environment. The primary goal is to achieve a globally optimal solution, effectively addressing the dynamic and strategic nature of adversarial attacks. The methodology initiates by creating a blockchain through a Markov decision process, defining states, actions, and rewards. Subsequently, a quantum deep recurrent network is applied to determine optimal Q-values in the structure, combined with hybrid Adaptive Group teaching Termite alate Optimization (AGTO), enhancing defense mechanisms adaptability against intelligent and evolving attackers in cryptographic systems to identify the global optimal solution. The experiments, conducted using Python, demonstrate significant reductions in the sum of steps essential to reach the optimal resolution. The experimental results highlight the proposed method's effectiveness, showcasing a 35.29% throughput and 39.9% success ratio. Additionally, the proposed method attained 37.86 (bits/Hz/J) EE improvement over other baseline methods. These findings underscore the robustness of HQL-AGTO in mitigating the impact of optimal block withholding attacks on blockchain networks.