Multi-level security-based optimal designs for secure game communications

In the ever-evolving landscape of online gaming, ensuring secure and efficient communication channels has become paramount. As online gaming continues to flourish, so do the challenges of maintaining secure communication channels. The surge in complexity and evolving cyber threats necessitates innovative solutions. The main aim of the research work is to overcome the existing gap by introducing a novel approach that combines the resilience of ResNet architectures with a multi-level security framework to reduce the complexity, increase the security in online gaming, etc. The increasing complexity of game communication systems, coupled with the rising sophistication of cyber threats, poses a significant challenge. Traditional security measures often fall short of providing comprehensive protection, leaving vulnerabilities that malicious actors exploit. This study addresses the complexity and threats associated with game communications by proposing an AES-ResNet-based threat mitigation framework with a multi-level security approach. Our approach leverages ResNet's deep learning capabilities to discern and mitigate threats effectively. The multi-level security framework involves the integration of encryption, authentication, and anomaly detection at different stages of game communication. The results demonstrate a significant reduction in system complexity and enhanced threat mitigation. The integration of ResNet architectures proves effective in identifying and neutralizing malicious activities, contributing to a more secure game communication environment. Further validation and refinement are ongoing, promising a robust solution for the evolving challenges in online gaming security.