BEMSQIN: Design of an efficient hybrid Bioinspired Encryption Model for enhancing Security of QoS-aware IoT Networks

Strength and Quality-of-Service (QoS) performance of encryption techniques like Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), etc. depends upon their internal key configurations. Researchers have proposed a wide variety of models to optimize the security of these models while maintaining high QoS via dynamic programming techniques. But these techniques cannot be scaled for context-specific deployments, and cannot be reconfigured to support large-scale IoT (Internet of Things) Networks. To overcome these issues, this text proposes design of an efficient & Novel Elephant Herding Ant Lion Optimizer (EHALO), which assists in identification of security models & their internal configurations for different contextual deployments. The proposed model integrates spatial security performance with temporal communication performance in order to decide which encryption model to use, and then fuses this information with temporal security measures in order to identify optimal security configurations. These configurations are tested on multiple data level attack scenarios including Spoofing, Grey Hole, and Masquerading & Man-in-the-Middle (MITM) during identification of these configurations. Due to which the model is able to mitigate attacks with high efficiency while maintaining 8.3% lower delay, 4.5% higher energy efficiency, 9.5% higher throughput, and 2.4% higher packet delivery performance when compared with existing dynamic encryption models on similar attack scenarios.