Securing internet of things using machine and deep learning methods: a survey
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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. © The Author(s) 2024.
Açıklama
Anahtar Kelimeler
Deep Learning, Internet Of Things, Machine Learning, Security
Kaynak
Cluster Computing
WoS Q Değeri
Scopus Q Değeri
Q2
