IoT Security: Threat Detection, Analysis and Defense

Dear Colleagues,

The Internet is gradually transforming from a communication platform for IT devices to the Internet of Things (IoT), which connects various devices and sensors together.

Internet of Things technologies are increasingly at the basis of modern communication, enabling the autonomous exchange of data between billions of physical and virtual objects, creating smart environments in sectors such as healthcare, logistics, etc.

However, the advancement of IoT communication also brings new security challenges. Managing the security of the IoT raises major concerns, especially when devices are deeply integrated into critical infrastructure, hospitals, and vehicles. Furthermore, the IoT is a key component of 5G/6G architectures (URLLC—Ultra-Reliable Low Latency Communications and critical communications) and Industry 4.0. All these technologies are designed to support critical applications.

The increased number of related potential attack vectors poses a substantial risk for malicious attackers. Also, the focus of security has been put on large-scale, software-oriented systems (such as the Cloud or datacenter systems) rather than embedded electronics. Consequently, the establishment of IoT ecosystems across different domains remains highly vulnerable to a wide range of threats.

This Special Issue aims to gather high-quality original research contributions and the latest research results in the field of threat detection within the IoT as well as cover threat analysis and corresponding defense techniques. The threats could be related to privacy issues, trust issues, IoT management issues, IoT intrusion, vulnerability issues, malware detection, cryptographic keys management, reliability of IoT communication (including secure routing aspects), IoT forensics techniques, Cloud-related IoT issues, etc.

Prof. Dr. Olivier Markowitch
Prof. Dr. Jean-Michel Dricot
Guest Editors

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Title: Feature Selection Enhancement to Evaluate Attack Detection in Internet of Things Environment
Authors: Khawlah M. Harahsheh; Rami M Al-Naimat; Chung-Hao Chen
Affiliation: Old Dominion University
Abstract: Feature selection in machine learning involves the process of selecting a subset of the most relevant features, which are the input variables or attributes, from the original set of features. The primary objective of this work is to enhance the efficiency, performance, and speed of feature selection methods for evaluating intrusion detection in wireless networks, with a particular focus on the Internet of Things (IoT) environment. In the context of IoT, several limitations, such as constraints related to capacity, power, and computational processing, pose significant challenges in effectively detecting intrusions. To address these challenges, this work develops a lightweight feature selection approach. This approach is designed to reduce the computational overhead on IoT resources while simultaneously strengthening the capabilities of intrusion detection within the IoT environment.