Securing internet of things using machine and deep learning methods: a survey

【Author】 Ghaffari, Ali; Jelodari, Nasim; Pouralish, Samira; Derakhshanfard, Nahide; Arasteh, Bahman

【Source】CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

【影响因子】2.303

【Abstract】The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people's daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges.

【Keywords】Internet of things; Machine learning; Deep learning; Security

【发表时间】2024 OCT

【收录时间】2024-09-23

【文献类型】案例研究

【主题类别】

区块链应用-实体经济-物联网