Integrating Blockchain and Deep Q-Learning for Adaptive, Trustworthy Cybersecurity Education

Abstract

The rapid evolution of cyber threats demands training solutions that are adaptive, scalable, and verifiable. Traditional cybersecurity training programs often follow static curricula, failing to accommodate differences in learners’ prior knowledge and skill development rates. This paper presents a novel framework that combines Deep Q-Learning (DQN) with blockchainbased credentialing to deliver personalized, trustworthy cybersecurity training within simulated cyber range environments. The proposed system models the training process as a Markov Decision Process (MDP), where each trainee’s state represents proficiency across cybersecurity domains such as intrusion detection, penetration testing, malware analysis, and incident response. The DQN agent dynamically selects the next optimal challenge-balancing exploration of new techniques with reinforcement of mastered skills-using performance metrics, error patterns, and engagement indicators. Milestone achievements are validated through smart contracts and recorded on a blockchain, ensuring that issued credentials are immutable, transparent, and portable across institutions. Experiments were conducted using a cyber range simulation dataset aligned with the NICE Cybersecurity Workforce Framework. Compared to baseline adaptive methods, the proposed framework reduced time-to-competency by 15%, increased challenge relevance by $10 \%$, and improved engagement by $14 \%$. Blockchain integration added less than 3% computational overhead while providing instant, verifiable credential issuance. These findings demonstrate that reinforcement learning can effectively personalize cybersecurity training while blockchain ensures the integrity and trustworthiness of skill certification. Beyond efficiency gains, the system addresses fairness by embedding constraints into the reward function to mitigate bias in recommendations. Future research will explore multi-agent cyber defense simulations, real-time threat intelligence integration, and blockchain interoperability standards for global credential verification. This work contributes to the intersection of AI, blockchain, and cybersecurity education, offering a scalable, ethical, and secure pathway to preparing the next generation of cybersecurity professionals.