ICSMPC: Design of an Iterative-Learning Contextual Side Chaining Model for Improving Security of Priority-Aware Cloud Resources

Purpose: A wide variety of encryption-based, key-exchange-based, privacy-based, and confidentiality-based models are proposed by researchers, which assist in enhancing security performance under real-time attacks. But most of these models are either unsalable due to their QoS (Quality of Service) performance under large loads, or showcase lower attack detection efficiency for heterogeneous attacks. Methods: To overcome these issues, this text proposes the design of a novel iterative learning contextual-sidechaining model for improving the security of priority-aware cloud resources. The proposed model initially uses a single-chained blockchain, which is split into multiple sidechains via Grey Wolf Optimization (GWO). These sidechains are reconfigured via a Bacterial Foraging Optimizer (BFO), which assists in deciding encryption and hashing techniques for individual chains. The BFO Model uses a resource-level priority metric, which assists in deciding the level of security and QoS for individual sidechains. The model is further extended by a Particle Swarm Optimizer (PSO) which assists in iteratively optimizing the GWO & BFO Models for QoS & Security enhancements. Results: Due to these enhancements, the proposed model is able to improve multiple QoS metrics even under heterogeneous attacks. The proposed model was tested under Sybil, distributed denial of service, masquerading, and spoofing attacks with multiple communication configurations. Conclusion: It was observed that the model was able to reduce the delay needed for communication by 8.5 %, improve the energy efficiency by 3.9 %, and increase the throughput by 4.5 %, and improve the request processing performance by 2.5 % under large-scale requests.