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.