Iotsdl: Internet Of Things Security For Deep Learning Techniques-A Research Perspectives

Abstract

The Internet of Things (IoT) is an important component of information technology. New trend technology is being developed during the Industnal 4.0 Revolution. IoT connects physical things from a variety of industries, including smart homes, wearable technology, vehicular ad hoc networks (VANETs), Healthcare and Smart Cities. The number of IoT applications has dramatically increased recently, and it is predicted that by 2030, there will be 131 billion linked devices, an increase of 13% each year on average. Security is a crucial issue in today’s IoT industry. In order to detect Internet of Things attacks and identify new types of intrusion to access a more secure network, using deep learning techniques in various models is a useful tool. This helps to overcome the challenges associated with securing IoT devices. The need for developing an attack-identification and classificationsystem for intrusion detection. We provide a study on the distinction between legitimate and malicious actions in order to distinguish abnormalities and intrusions, as well as Network traffic analysis to find new threats. This paper provides broad reviews of deep learning for Internet of Things Seurity. The Major contributions are helpful for researchers and academicians for further research in the direction of Internet of Things Security. This reviews studies by assessing their effectiveness using two kinds of fresh real-time traffic information. (i.e. Bot-IoT datasets and CSE-CIC-IDS2022). We evaluate ‘accuracy rate’, ‘ detection rate’ in many system.