Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

After the emergence of the Internet of Things (IoT), the way devices interact with each other changed, as it allowed automation and seamless communication in various fields. However, various challenges related to security and trust have emerged, hindering the widespread adoption of the IoT. Blockchain technology is considered the ideal solution to face these challenges because of its immutable and decentralized nature. This paper explores the potential of blockchain technology to address critical security and trust challenges within the rapidly growing IoT ecosystem. Through a systematic literature review, this study examines how blockchain’s decentralized, immutable, and transparent features contribute to enhancing security and trust in IoT networks. Key findings indicate that blockchain integration can prevent data manipulation, ensure robust identity management, and facilitate transparent, verifiable transactions, supporting both security and trust in IoT systems. These attributes not only improve IoT security but also promote sustainable practices by optimizing resource efficiency, reducing environmental impact, and enhancing resilience in systems like supply chain management and smart grids. Additionally, this study identifies open research challenges and suggests future directions for optimizing blockchain in IoT environments, focusing on scalability, energy-efficient consensus mechanisms, and efficient data processing.

The integration of the Internet of Things (IoT) and cloud computing, which play essential roles in our everyday routines, is expected to emerge as a fundamental element of the forthcoming internet, realizing increased usage and acceptance. This fusion is anticipated to revolutionize various applications, offering The integration of IoT and cloud may pose challenges. Cloud computing’s capacity to distribute resources and data across diverse locations, facilitating access from different industrial settings, has significantly enhanced IoT functionality. However, rapid migration to the cloud has raised security concerns, as conventional security measures for computers are not always applied effectively to cloud-based systems. Overcoming these obstacles can be achieved by integrating cloud and IoT technologies, as the vast resources available on the cloud can greatly benefit IoT, helping the cloud transcend current limitations related to physical objects in a more dynamic, distributed manner. Several discoveries from the research were made by exploring the facilitation of a smooth shift of IoT initiatives to the cloud by studying IoT and cloud computing, investigating various cloud-related challenges and resolutions derived from recent scholarly works, and analyzing the most recent advancements in attacks targeting cloud-based IoT systems. Identifying gaps in the research on IoT-based cloud infrastructure and addressing cybersecurity in cloud computing is important for future research directions, necessitating a review of the technological challenges mentioned in the literature. As such, this research explores how blockchain technology effectively addresses security concerns within this combination, emphasizing its capacity to improve data integrity and privacy and to ensure secure transactions. The exploration delves into the multifaceted implications and potential applications of blockchain, elucidating its role in reinforcing the overall security of these interconnected systems.

This systematic literature reviewexamines existing literature to determine howDigital Twin (DT) technology can enhance security, with an emphasis on emerging issues, approaches, and proposals in cybersecurity. The review categorizes selected studies based on the techniques used to protect DTs: Machine Learning (ML) and non-machine learning techniques. DTs are classified into five types: component level, process level, asset level, system level, and network of systems level, each with unique applications and corresponding cybersecurity threats. The main conclusions highlight medium to very high perceived risks depending on the DT type and industry implementation. Results identify the most common types of attacks on DTs, various security enhancement techniques, and the sectors that utilize DTs. Future research should focus on developing more effective security paradigms, technologies, and integration schemes to reduce risks and maximize DT technology’s potential. This review presents the current status of DT-related cybersecurity and explores ways to enhance security against emerging cyber risks.

Smart cities have witnessed a transformation in urban living through the Internet of Things (IoT), which has improved connectedness, efficiency, and sustainability. However, the adoption of IoT devices presents significant security vulnerabilities, particularly in authentication. The specific limitations of IoT contexts, such as constrained computational resources, are frequently not adequately addressed by traditional authentication techniques. The existing methods of authentication used for IoT devices in smart cities are critically examined in this review study. We evaluate the advantages and disadvantages of each mechanism, emphasizing real-world applicability. Additionally, we examine cutting-edge developments that offer improved security and scalability, such as blockchain technology, biometric authentication, and machine learning-based solutions. This study aims to identify gaps and propose future research directions to develop robust authentication frameworks that protect user privacy and data integrity.

The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms.