IoT-Deep Learning-Based Detection of Cyber Security Threats

Abstract

The internet of things (IoT) is a revolutionary technology that links living and non-living devices all around the world. As a result, the frequency of cyber-attacks against IoT deployments is expected to rise. As a result, each system must be absolutely secure; otherwise, consumers may opt not to utilize the technology. DDoS assaults that recently attacked various IoT networks resulted in massive losses. There is only one way to detect stolen data from software and malware on the IoT network that is discussed in this chapter. To categorize stolen programming with source code literary theft, the tensor flow deep neural system is offered. To communicate raucous information while simultaneously emphasizing the value of each token in terms of source code forgery, tokenization, and measurement, the malware samples were gathered using the Malign dataset. The results show that the methodology proposed for analyzing IoT cyber security threats has a higher classification efficiency than current methodologies.