Centralized, Distributed, and Everything in between

One of the major needs and challenges of this century is the use of cutting-edge technology considering the industry 4.0 revolution. The Internet of Things (IoT) falls in the category of a cutting-edge example of such innovation in the computing and information industry. In IoT compared to classical networking methods practically; every device we employ is accessible at any time from any location. Nevertheless, IoT continues to encounter several security challenges, and the magnitude of cyber-physical security risks is escalating alongside the widespread use of IoT technologies considering Moore’s laws expected to be 30 billion devices by 2025. IoT will continue to face vulnerabilities and risks unless there is a comprehensive understanding and proactive approach towards tackling its security concerns. To ensure both the cyber and physical security of IoT devices during data gathering and sharing, it is imperative to evaluate security considerations, identify instances of cyber-attacks, and implement effective security protocols at multiple layers for making highly secured IoT. Conventional security measures like data classification, strict access controls, monitoring privileged account access, encrypting sensitive data, security awareness training, network segregation, segmentation cloud security, application security, patch management, and physical security employed in the realm of IoT are inadequate in light of the current security difficulties posed by the proliferation of sophisticated attacks and threats. Utilization of artificial intelligence (AI) techniques, especially machine and deep learning models is becoming a compelling and effective approach to enhance security of the IoT devices. This research article presents a comprehensive review of the key aspects of IoT security, including the challenges, potential opportunities, and AI-driven solutions. The primary goal of this article is to provide technical resources for cybersecurity experts and researchers working on IoT initiatives.

In this modern era, we are habituated to small smart home appliances that are typically known as the Internet of Things (IoT) devices. While these devices make our daily life easier with the help of internet connectivity, they are becoming an easy target for attackers to penetrate various cyber-attacks. Distributed Denial of Service (DDoS) is one of the pernicious attacks that can be attempted easily on IoT devices. To make our home smart, these devices play a vital role and as a security expert, it is our responsibility to ensure the security of these IoT devices. In this paper, we propose a security scheme to ensure the IoT devices' security. With the capability of the ensemble machine learning technique, we designed and implemented an intrusion detection system in securing IoT devices. To utilize the full capability of our security solution, we verified it with various well-known and benchmark datasets including the IoT dataset. The results show the efficiency of our security solution by producing better F-1 score and lower false alarms compared to any existing approaches.

Recent growth in the Internet of Things (IoT) looks promising for realizing a smart environment of the future. However, concerns about the security of IoT devices are escalating as they are inherently constrained by limited resources, heterogeneity, and lack of standard security controls or protocols. Due to their inability to support state-of-the-art secure network protocols and defense mechanisms, standard security solutions are unsuitable for dynamic IoT environments that require large and smart IoT infrastructure deployments. At present, the IoT based smart environment deployments predominantly use cloud-centric approaches to enable continuous and on-demand data exchange that leads to further security and privacy risks. While standard security protocols, such as Virtual Private Networks (VPNs), have been explored for certain IoT environments recently, the implementation models reported have several variations and are not practically scalable for any dynamically scalable IoT deployment. This paper addresses current drawbacks in providing the required flexibility, interoperability, scalability, and low-cost practical viability of a secure IoT infrastructure. We propose an adaptive end-to-end security model that supports the defense requirements for a scalable IoT infrastructure. With low-cost embedded controllers, such as the Raspberry Pi, allowing for the convergence of more sophisticated networking protocols to be embedded at the IoT monitoring interface, we propose a scalable IoT security model integrating both the IoT devices and the controller as one embedded device. Our approach is unique, with a focus on the integration of a security protocol at the embedded interface. In addition, we demonstrate a prototype implementation of our IoT security model for a smart water monitoring system. We believe that our modest first step would instill future research interests in this direction.

With the rapid growth of Internet-of-Things (IoT) devices, security and privacy issues emerged as a potential roadblock for widespread adoption. Preliminary research indicates that many types of IoT devices have serious vulnerabilities. It is not easy to investigate security and privacy issues since each type of device is different and manual experiments need to be conducted on the device. In this paper, we propose a framework for investigation of security and privacy issues of IoT devices. The framework consists of four components, a testbed, set of topics to be investigated, a set of experiments for each topic investigated and a final report. Fundamental approach used in the framework is to capture layer 2 and layer 3 packets and to analyze the packets for various features. Proposed framework is low cost and is based on off-the-shelf hardware and open source software. Using the framework, we can investigate security and privacy issues of many IoT devices including HDMI sticks, IP cameras, activity trackers, smartwatches and drones. A large set of topics can be investigated on IoT devices using the framework including vulnerability issues, protocol security, firmware updates, authentication issues and privacy violations. Sample experimental results show the promise of the proposed framework. We believe this framework will serve as the foundation for a general automated framework to investigate security and privacy issues of most IoT devices.

Background: The increasing number of Internet of Things (IoT) devices has raised worries about their susceptibility to different cyber threats. Although there have been improvements in software-level security, hardware measures continue to be essential for strong protection. Objective: The purpose of this study is to thoroughly analyze different architectural methods for ensuring hardware security in IoT devices, assessing how effective and practical they are. Methods: The study conducted a comparative investigation of different hardware security mechanisms, such as Trusted Platform Modules (TPMs), Physical Unclonable Functions (PUFs), and hardware security modules (HSMs). Information was gathered from several Internet of Things (IoT) implementations in industrial, healthcare, and consumer industries. Results: The results suggest that PUFs have notable benefits in terms of distinctiveness and protection against physical tampering, while TPMs offer extensive security capabilities but at a greater expense and level of complexity. High-security modules (HSMs) were determined to be the most effective choice for environments with high stakes that demand strict security measures. Conclusion: The selection of a suitable security method for IoT applications depends on the particular requirements and hazards associated with each application, as there is no one-size-fits-all hardware security mechanism. This paper establishes a fundamental structure for choosing and executing hardware security measures in IoT designs, improving the durability of devices against advancing cyber threats.

Recently, the security of internet of things (IoT) devices which include consumer electronics has attracted attentions. Since an authenticated encryption can realize the encryption and the authentication simultaneously, it is effective for the security of IoT devices. Regarding security, the risk of hardware Trojan is pointed out. To ensure the security of IoT devices, the research of hardware Trojan for the authenticated encryption is very important. Therefore, this study proposes a new hardware Trojan for Minalpher which is one of the most popular authenticated encryptions. Experiments examine the risk of the proposed hardware Trojan.

Through the Internet of Things (IoT), everything is connected remotely through a network, allowing smart environments to be created. The IoT will play a central role in every aspect of our daily lives, from toys to healthcare. The security of IoT devices is the most crucial concern. In this chapter, we will discuss hardware vulnerabilities in IoT devices that pose major threats to data transfer. Researchers focus on finding the major vulnerabilities and limitations of proposed countermeasures for IoT devices’ hardware security issues. Modern electronic hardware used in IoT systems consists mostly of interconnected integrated circuits, so hardware security directly affects integrated circuit security. Malicious modifications to integrated circuits are carried out in untrusted fabrication units by Hardware Trojans. Since these Hardware Trojans are stealthy, most of the detection schemes require a gold chip for measuring the side channels. In the existing Trojan detection schemes, false-positive and false-negative detections are possible because of process variation. As a result, this chapter provides an overview of the hardware security challenges with Hardware Trojans associated with IoT devices used in smart environments.

The inclusion of Internet of Things (IoT) for building smart cities, smart health, smart grids, and other smart concepts has driven data-driven decision making by managers and automation in each domain. However, the hyper-connectivity generated by IoT networks coupled with limited default security in IoT devices increases security risks that can jeopardize the operations of cities, hospitals, and organizations. Strengthening the security aspects of IoT devices prior to their use in different systems can contribute to minimize the attack surface. This study aimed to model a sequence of seven steps to minimize the attack surface by executing hardening processes. Conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) techniques. In this way, we were able to define a proposed methodology to evaluate the security level of an IoT solution by means of a checklist that considers the security aspects in the three layers of the IoT architecture. A risk matrix adapted to IoT is established to evaluate the attack surface. Finally, a process of hardening and vulnerability analysis is proposed to reduce the attack surface and improve the security level of the IoT solution.

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.

The Internet of Things (IoT) has rapidly expanded across multiple sectors, exposing significant opportunities but also raising important concerns. This rapid growth has raised concerns about the security of IoT devices and the protection of the large volumes of data they collect, transmit, store, and process. Numerous large-scale attacks on IoT systems underscore the need for security measures, as well as comprehensive security assessments and benchmarking methods to verify and validate these systems. We conduct a Systematic Literature Review (SLR) to analyze previous studies, methodologies, and tools used to assess and benchmark the security of IoT systems, and to identify critical challenges and gaps in the existing literature. As a result, we highlight that, due to their complexity, IoT systems lack a comprehensive security framework that covers all layers and their security concerns. Despite awareness of known vulnerabilities, there is a lack of best practices, tools, and techniques to prevent, detect, and mitigate threats effectively. The absence of standardized security benchmarks complicates the evaluation and comparison of the solutions. There is also limited alignment with emerging standards such as ISO/IEC 27402 and SESIP. Finally, it is noteworthy that IoT gateway security remains unexplored despite its critical role in IoT ecosystems. CCS Concepts: • Computer systems organization → Embedded systems ; Redundancy ; Robotics; • Networks → Network reliability.

The internet of things (IoT) is a multiple devices, which connects with the internet for communication, in order to obtain the updated from the cloud. The fog can act as a controller and it is located between the IoT devices and cloud. The major attacks like de-synchronization, and disclosure has arises in the devices, this has been prevented. The major contribution in this work is key generation and authentication, for key generation the “advanced encryption standard algorithm” is developed, in which the new and old keys are generated. The encryption is done under the source side, and decryption is done under the device side. The fog security is maintained through “device tag, and bit wise XOR rotational algorithm”. The security, and the computational complexity is defined in this work and it is given in table format. The implementations are carried out in the MATLAB R2016 a. The proposed algorithm is compared with the existing protocols like LMAP, M2AP, EMAP, SASI, and RAPP, from the comparison the proposed methodology makes the better knowledge about the security and prevents from various attacks.

The Internet of Things (IoT) has become very popular during the last decade by providing new solutions to modern industry and to entire societies. At the same time, the rise of the industrial Internet of Things (IIoT) has provided various benefits by linking infrastructure around the world via sensors, machine learning, and data analytics. However, the security of IoT devices has been proven to be a major concern. Almost a decade ago, the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was designed to handle routing in IoT and IIoT. Since then, numerous types of attacks on RPL have been published. In this paper, a novel intrusion detection system (IDS) is designed and implemented for RPL-based IoT. The objective is to perform an accurate and efficient detection of various types of routing and denial-of-service (DoS) attacks such as version number attack, blackhole attack, and grayhole attack, and different variations of flooding attacks such as Hello flood attack, DIS attack, and DAO insider attack. To achieve this, different detection strategies are combined, taking advantage of the strengths of each individual strategy. In addition, the proposed IDS is experimentally evaluated by performing a deep analysis of the aforementioned attacks in order to study the impact caused. This evaluation also estimates the accuracy and effectiveness of the IDS performance when confronted with the considered attacks. The obtained results show high detection accuracy. Furthermore, the overhead introduced in terms of CPU usage and power consumption is negligible. In particular, the CPU usage overhead is less than 2% in all cases, whereas the average power consumption increase is no more than 0.5%, which can be considered an insignificant impact on the overall resource utilisation.

IoT security: challenges and countermeasures

The Internet of Things (IoT) is one of the most extensive technological evolution of the computing network. This technology can transform the physical world into a virtual world for testing and emulation to evaluate the key issues present in the physical devices. This work aims to explore the security in IoT devices and demonstrates the security gaps in the behavior of the smart door lock. In this paper, we conducted two surveys to gather consumers’ requirements about the IoT devices as to whether they do understand the security risks involves with these devices. Further, we carried out a denial of service attack on a smart lock device to demonstrate that such devices are not secure. This work also highlights the security weakness and suggest guidelines to improve the overall system using cloud and edge computing and authentication and access control-based solutions.

In recent years, the development of the Internet of Things technology has been very rapid. However, with the explosive growth of IoT devices, the challenges facing the IoT environment are becoming more and more serious. How to ensure the security of the IoT becomes a key issue. The primary purpose of IoT security is to protect private data and ensure the security of IoT users, infrastructure, data and devices. This paper reviews the threat challenges and IoT security models at each level of the Internet of Things in recent years and discusses some of the past and future solutions. At first, the IoT security threats are systematically introduced from the perspectives of physics, network and data. In addition, the mainstream IoT security model is introduced. Then we introduce solutions to IoT threats from three perspectives, including IoT access control, intrusion detection and distribution methods. In the end, the paper introduces the current IoT security model has not formed a unified standard and expresses a future expectation for the development of future security model.