Machine learning based solutions for security of Internet of Things (IoT): A survey
The architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks, and ML-based potential solutions for IoT security has been presented and future challenges are discussed.
Abstract
Over the last decade, IoT platforms have been developed into a global giant\nthat grabs every aspect of our daily lives by advancing human life with its\nunaccountable smart services. Because of easy accessibility and fast-growing\ndemand for smart devices and network, IoT is now facing more security\nchallenges than ever before. There are existing security measures that can be\napplied to protect IoT. However, traditional techniques are not as efficient\nwith the advancement booms as well as different attack types and their\nsevereness. Thus, a strong-dynamically enhanced and up to date security system\nis required for next-generation IoT system. A huge technological advancement\nhas been noticed in Machine Learning (ML) which has opened many possible\nresearch windows to address ongoing and future challenges in IoT. In order to\ndetect attacks and identify abnormal behaviors of smart devices and networks,\nML is being utilized as a powerful technology to fulfill this purpose. In this\nsurvey paper, the architecture of IoT is discussed, following a comprehensive\nliterature review on ML approaches the importance of security of IoT in terms\nof different types of possible attacks. Moreover, ML-based potential solutions\nfor IoT security has been presented and future challenges are discussed.\n