Novel intelligent trust computation for securing internet-of-things using probability based artificial intelligence

Abstract

With rising demands of smart appliances with normal locations transforming themselves in smart cities, internet-of-things (IoT) encounters various evolving security challenges. The frequently adopted encryption-based approaches have its own limitation of identifying dynamic threats while artificial intelligence (AI) based methodologies are found to address this gap and yet they too have shortcomings. This manuscript presents an intelligent trust computational scheme by harnessing probability-based modelling and AI-scheme for monitoring the dynamic malicious behavior of an unknown adversaries. The study contributes towards a novel AI-model using reinforcement learning towards leveraging decision making for confirming the presence of unknown adversaries. The benchmarked study shows that proposed system offers significant improvement when compared to existing AI-models and other cryptographic schemes with respect to delay, throughput, detection accuracy, execution duration.