IoT Security and Privacy Using Deep Learning Model: A Review
K. Janani, S. Ramamoorthy · 2021 International Conference on Intelligent Technologies (CONIT) · 2021
Today, the worldwide Internet of Things is one of the emerging technologies and very useful for all smart handling things like (home, smart meter, parking, infrastructure surveillance, healthcare, and government, maritime, banking, smart communication, smart vehicles) etc This makes our life easier every day. IoT is a centralized device that anyone can access, but it is still vulnerable to security attacks such as botnets, DDoS/DoS, malicious attacks, and Shinhole attacks, which IoT must address. These issues include the use of a large number of heterogeneous devices, which compromises security and scalability, the need for more energy efficient devices, and the centralized system that anyone can access, making it easy for attackers. The aim of this paper is to provide a comprehensive discussion of deep learning algorithms and standard dataset in IoT for security and privacy concerns. Our primary focus is on enhancing IoT security through deep learning. First, we look at deep learning algorithms and classification in IoT security from the standpoint of device design and methodologies. Second, we examine the suitability of IoT systems in terms of security.