The Internet of Things (IoT) aims to transform everyday physical objects into an interconnected ecosystem with digital data accessible anywhere and anytime. “Things” in IoT are embedded with sensing, processing, and actuating capabilities and cooperate in providing smart and innovative services autonomously. The rapid spread of IoT services arises different security vulnerabilities that need to be carefully addressed. Several emerging and promising technologies and techniques are introduced to improve the security of IoT. This paper aims to provide an up-to-date vision of the current research topics related to IoT security. Initially, we introduce common elements and protocols of IoT to demystify the origins of threats in IoT. Then, we propose a taxonomy of IoT attacks and analyze the security vulnerabilities of IoT at different layers. Subsequently, we provide a comparison of recent security schemes based on emerging solutions including fog computing, edge computing, software-defined networking (SDN), blockchain, lightweight cryptography, homomorphic and searchable encryption, and machine learning. Finally, security challenges are discussed and future directions are highlighted for future interested researchers.

The internet of things (IoT) is expected to offer a significant impact on the industry domain leading to the concept of industrial IoT (IIoT). The IIoT comprises machine-to-machine (M2M) and communication technologies with data automation and exchange to improve product quality and decrease pro-duction costs. As a consequence, a large amount of data is collected and smartly processed to provide optimal industrial operations. This growing deployment enables adversaries to con-duct potential and destructive cyber-attacks to accomplish their malicious goals. Therefore, intelligent decision-making actions for cyber-attack detection in IIoT are sorely required. To address this challenge, we propose an intrusion detection system (IDS) using deep learning models. Specifically, the proposed system is based on the combination of convolutional neural network (CNN) and long short-term memory (LSTM) that are excellent techniques for intrusion detection and classification due to their ability in classifying main characteristics and their effectiveness in performing faster computations. We adopt the most recent dataset named Edge-IIoTset that contains a real traffic network of IoT and IIoT applications. The proposed model is evaluated in terms of accuracy, precision, false positive rate, and detection cost within binary and multi-class classifications. The obtained results show that our CNN-LSTM model provides better performance and robustness in cyber security intrusion detection for IIoT applications compared to LSTM and traditional machine learning models. Moreover, it outperforms two recent related models in terms of accuracy rate.

Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significant advancements across various domains, such as making IoT-based security systems smarter and more efficient. However, ML-based IoT systems are vulnerable to lurking attacks during the training and testing phases. An adversarial attack aims to corrupt the ML model’s functionality by introducing perturbed inputs. Consequently, it can pose significant risks leading to devices’ malfunction, services’ interruption, and personal data misuse. This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context. A comprehensive classification of adversarial machine learning (AML) is provided. Moreover, a systematic literature review of the latest research trends (from 2020 to 2024) of the intersection of AML and IoT-based security systems is presented. The results revealed the availability of various AML attack techniques, where the Fast Gradient Signed Method (FGSM) is the most employed. Several studies recommend the adversarial training technique to defend against such attacks. Finally, potential open issues and main research directions are highlighted for future consideration and enhancement.