IoT Security Techniques Based On Machine Learning: How IoT Devices use AI to Enhance Security
Lakshmisri Surya · SSRN Electronic Journal · 2019
The internet of things (IoT) integrates various devices within the network to come up with intelligent and advanced services that help protect the privacy of the user and attacks, which includes eavesdropping, jamming, denial of service (DOS) attacks, and spoofing attacks. This research paper investigates the model used by IoT systems in cooperation with AI to enhance devices' security. The report also reviews IoT security solutions focused on machine learning technology, which includes reinforcement of learning, unsupervised learning, and supervised learning. The article also focuses on IoT malware detection, secure offloading, access control, and authentication techniques based on machine learning, which is AI. Additionally, the paper addresses the challenges which need to be investigated and addressed when implementing these machine learning schemes of security in IoT systems practically. IoT celebrated as the next technological revolution's enabler would involve easy accessibility across extreme security, complex service mobility and context-aware, and cellular network networks. Thus, AI can play a significant role in the technology of the network infrastructure. However, by using the principles, instruments, and technologies of AI in cellular connections used by IoT, a range of problems will surface. The fundamental issues in using AI in wireless information systems that enable end-to-end IoT connectivity with possible comprehensive future research directions and solutions are addressed in this paper.