TY - JOUR
AU1 - Ghaffari, Ali
AU2 - Jelodari, Nasim
AU3 - pouralish, Samira
AU4 - derakhshanfard, Nahide
AU5 - Arasteh, Bahman
AB - 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.
TI - Securing internet of things using machine and deep learning methods: a survey
JF - Cluster Computing
DO - 10.1007/s10586-024-04509-0
DA - 2024-10-01
UR - https://www.deepdyve.com/lp/springer-journals/securing-internet-of-things-using-machine-and-deep-learning-methods-a-0fo3C4HqDv
SP - 9065
EP - 9089
VL - 27
IS - 7
DP - DeepDyve
ER -