A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

Abstract

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration.Povzetek: Podan je pregled uporabe strojnega in globokega učenja v IoT napravah ter izzivov, rešitev in prihodnjih smeri raziskav.Informatica 47 (2023) 1-54 k. Harahsheh et al.

Machine Learning in Communication Systems and Networks

“…The increasing volume and diversity of data in dynamic communication systems demand innovative approaches for efficient operation and optimal performance. From predicting environmental or system status changes to optimizing resource allocation and addressing security threats [ 7 ], ML spans applications like intelligent traffic management [ 8 ] and automatic reconfiguration in communication infrastructure [ 9 , 10 ].…”

Using Feature Selection Enhancement to Evaluate Attack Detection in the Internet of Things Environment

“…Traditional feature selection methods face optimization difficulties and have high computational complexity [2]. The limitations of IoT devices lead to a higher likelihood of a?acks or flaws in their security protocols [3]. These limitations and challenges of IoT encompass various It becomes imperative to identify and select the most relevant data features to enhance the performance of a machine learning model, particularly for IDSs in IoT devices where securing these environments with conventional algorithms and techniques poses a challenge.…”

“…Traditional feature selection methods face optimization difficulties and have high computational complexity [2]. The limitations of IoT devices lead to a higher likelihood of attacks or flaws in their security protocols [3]. These limitations and challenges of IoT encompass various categories, including hardware constraints, software issues, network concerns, and security vulnerabilities.…”