A review on machine learning techniques for secure IoT networks

Abstract

Internet of Things (IoT) is the major technology of the 4 th industrial revolution in which various types of devices are connected together to work smartly without the intervention of humans. IoT seems to impart a great impact on our social, economic, and commercial lives. IoT applications are converting from smart home and smart me to the smart cities or smart planet. However, the large number of devices interconnected with each other by multi protocols puts the security of IoT networks on the verge of threats. Making the IoT devices more secure is also not feasible because of their limited computational power. Hence, there is a need for advancement in methods to secure IoT networks. Machine Learning (ML) models have been hot topics in security research in past years. As the IoT devices generate tons of data on a daily basis which can be used to train ML algorithms, it could be a reasonable solution to provide security to IoT systems. In this work, the main goal is to provide a broader survey of research works in the IoT security field regarding ML implementation. We briefly described the security issues in IoT networks and their impact on the privacy of important data. We then shed light on different ML algorithms and models and discussed their advantages, disadvantages, and applications in IoT individually. Moreover, the ML models currently working in IoT networks for security purposes are discussed. We also talked about the limitations of using ML models to secure the IoT networks which could provide new future research directions.

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

“…Security features must therefore be designed and refined. A data-conditionality-reduction technique is essential because security features and the IoT data they are associated with have a direct impact on machine-learning-based security models [ 40 ]. “Feature engineering” refers to the process of developing and refining security features.…”

“…In the last few years, security studies have paid a lot more attention to machine learning models. There may be a need for security for IoT systems because these devices regularly produce huge amounts of data that can be used to train machine learning algorithms [ 40 ]. New product components are developed using feature selection and principal component analysis, which together account for the majority of the significant data.…”

“…Nonetheless, it struggles to deal with difficult problems. In addition, you can only apply these tactics for select types of blows, and they are not extremely accurate [ 40 ]. Right now, one of the biggest problems with the Internet of Things is that we do not fully comprehend how apps use data.…”

IOT Based Privacy Preserving Machine Learning Algorithm for High-Speed Encryption

“…Purchasing more equipment is important for cybersecurity teams in order to keep track of all the devices and maintain network security, according to the National Cybersecurity Alliance. [22] Using high-level machine learning, according to the researchers, it is possible to contribute to the protection of the Internet of Things by automating the scanning and administration of IoT devices throughout the whole network [23][24]. When they scan all devices linked to a network, they may identify potential threats and shut them down quickly, before IT professionals are even aware of the situation.…”