Neural-driven dynamic key generation: A deep reinforcement learning and topological mathematics based framework for secure file sharing

The growing prevalence of data breaches in digital communication and platforms necessitates advanced mechanisms to secure adaptive file exchange systems. Traditional file-sharing methods reliant on static keys face challenges in maintaining security and adaptability in dynamic environments, which are increasingly susceptible to modern cyber threats. To address these challenges, a distinct computational framework was developed by integrating neural networks and deep reinforcement learning (DRL) to build a cryptographically secure pseudorandom number generator (CSPRNG) used to derive reliable and dynamic session-based keys, and algebraic knot theory–based topological transformations to improve cryptographic resilience and prevent key recovery in case of interception. This approach enables the framework to regularly update session-based keys to bypass vulnerabilities inherent in static systems while applying complex mathematical structures that advance resilience against cyberattacks. The framework’s efficacy has been validated through multiple experiments, including entropy and randomness tests, hamming distance analysis, and performance benchmarks that confirm both unpredictability and negligible runtime overhead. These results demonstrate that the framework achieves strong security properties while maintaining practical efficiency. Although the primary focus is on secure file sharing, the results confirm that the framework is efficient, adaptable, and practical within this domain, showing promise as a foundation for future exploration in other secure communication settings such as mobile, distributed environments and lightweight platforms.