Security and Privacy for the Internet of Things

The developers and users of Internet of Things (IoT) systems and devices bear a collective obligation to ensure that IoT systems and devices do not make the Internet and other users vulnerable to any potential harm. This chapter proposes a novel network secure framework that can be adapted to the future IoT and finally gives details for the security verification system design. This framework should focus on enhancing the efficacy, dependability, accountability, and regulation in the overall structure that checks the security issues. The chapter discusses the structuring of a security authentication system in the field of IoT. It presents the factors that pose a threat to the IoT security. These include Unprotected Network services, Cloud, mobile and an absence of security configuration. The chapter illustrates the essential components of IoT security and lists the potential challenges in terms of IoT security. It also discusses the concepts representing the risks that increase the probability of intrusions in the IoT devices.

A survey of trust computation models for service management in internet of things systems

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

The Internet of Things (IoT) has revolutionised the way people live and work, and its increasing popularity has led to the widespread adoption of smart homes. However, the lack of interoperability among IoT devices and systems used in smart homes limits their capacity to enhance the user experience and improve energy efficiency. Therefore, this study focuses on improving interoperability between various IoT systems and devices. It was anticipated that the research would contribute to the creation of a more cohesive and integrated smart home environment by helping to develop a data analytics framework that enables the integration and analysis of data from various devices. The study then produces an intended user design strategy for smart homes that takes into account users' wants, preferences, and behaviours during the design phase. The goal of this project was to create smart houses that are more intelligent, environmentally green, and user-friendly and that will benefit both individuals and the whole society.

Deep learning-enabled anomaly detection for IoT systems

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main objective of this paper is to examine potential solutions to the security problems that arise in IoT environments. It includes a thorough analysis of recent research and technological innovations in the field, with a particular emphasis on how different security methods are applied across IoT systems. By identifying the most common security vulnerabilities and outlining their impact on IoT networks, the review suggests improved methods to safeguard IoT data and ensure privacy. The findings aim to support researchers, developers, and businesses in designing more secure IoT solutions, and contribute to the establishment of stronger data protection policies. Ultimately, the review serves as a resource for those seeking to enhance the security of IoT devices and systems in an increasingly interconnected world.

In recent years, the Internet of Things (IoT) has attracted attention according to the development of sensing technology and wireless communication technology. IoT devices are interconnected to realize a specific service with sensors and actuation functions over several communication methods. Internet Protocol (IP) exists as a basic communication technology for realizing an IoT system. Since IPv4 is a mainstream protocol on the Internet, the lack of compatibility between IPv4 and IPv6 should be great issues to realize IoT services. Additionally, recent networks employ the Network Address Port Translation (NAPT) technology to protect a network and to reduce the number of required IPv4 global addresses. The introduction of NAPT causes an accessibility issue due to the blocking of incoming packets. As a new issue, the mobility of IoT devices also becomes a concern about seamless communication because a change of IP address causes a disconnection of transport layer sessions. As a technique for solving these problems, the authors have developed CYber PHysical Overlay Network over Internet Communication (CYPHONIC) that supports accessibility and seamless mobility over the Internet. Since high-performance IoT devices typically implement the full IP stack, both IPv4 and IPv6 can be supported. Therefore, CYPHONIC can provide inter-connectivity among IoT devices. On the contrary, resource-constrained IoT devices with a low power operation support only the limited IP stack such as IPv6 over Low power Wireless Personal Area (6LowPAN). This paper proposes a new interconnectivity mechanism between IP networks and 6LowPAN by extending CYPHONIC. Therefore, the proposed mechanism can provide interconnectivity among 6LowPAN devices and IP devices to collaborate on a service. The proposed system introduces a gateway device for connecting between IP networks and 6LowPAN. Additionally, it also extends the signaling process of CYPHONIC to support 6LowPAN based IoT devices. Since the object of this paper is confirming the feasibility of the proposed mechanism, we have designed the implementation with Thread protocol that support 6LowPAN.

As location-aware IoT (Internet of Things) devices intend to spread across almost every application, IoT devices are going to require different GNSS (Global Navigation Satellite System) receiver design requirements. To deliver on IoT opportunities, system architects and designers need to look carefully at every aspect of system design and architecture to be able to enable the full potential of the IoT era. IoT devices are equipped with different connectivity sensors that can provide a reliable integrated location-awareness solution. IoT devices are mainly relying on GNSS chip to deliver a service when no other RF signal is available. Hence, GNSS chip is expected to maintain a robust and reliable performance even in harsh environment. The harsh environment includes multipath environment, NLOS (Non-Line of Sight) GNSS signal reception, weak GNSS signal power reception, superimposed motion (e.g., arm swing, floating platform fluctuation …etc.), operation under freezing temperature, and interference. The targeted low-cost system design along with the expected harsh operational environment derived us to face exceptional GNSS signal reception condition. This paper discusses lessons learned, and challenges faced signal acquisition, reacquisition, and tracking for several IoT applications. Different operational modes are also explored to accommodate various use cases scenarios for several IoT applications. These different power consumption operational modes allow the GNSS chip to achieve an Ultra-low power consumption which indeed required for IoT applications. This paper provides an overview about the addressed GNSS chip fabricated in a 28nm FD-SOI process. We demonstrate a system performance and power consumption comparison of this chip along with other state-of-the-art GNSS chip. We compare the positioning performance for this device alongside another state-of-the-art GNSS modules and Novatel’s tightly coupled GNSS/INS (Inertial Navigation System) reference solution at numerous scenarios ranging from walking, running, and driving at several sites under different environment.

As the Internet of Things (IoT) systems become more and more popular, many devices around life are now connected to the internet. While allowing large amounts of information to be transmitted and exchanged, sensitive information in IoTs also becomes vulnerable to leakage. Therefore the security of each device and network in IoT systems has become a critical issue. Many IoT devices are simple devices used for daily supplies; they cannot cost too much and must be lightweight. These devices are often controlled by simple Operating Systems (OS), some even in bare-metal environments without OS. In this paper we propose a lightweight memory security mechanism implemented mainly by hardware. This mechanism puts critical data in secure regions, and the processor can access the secure regions only after passing a hardware-based authentication process. Our method allows the processor to maintain high security without relying on the OS; hence even if the IoT device only has a bare-metal environment, it still can protect important data at a low cost. Experimental results show that a very high level of security can be achieved with only a very small extra delay and area overhead required.

Since the start of Bitcoin in 2008[1], blockchain technology emerged as the next revolutionary technology. Though blockchain started off as a core technology of Bitcoin, its use cases are expanding to many other areas including finances, Internet of Things (IoT), security and such[2]. Currently, many private and public sectors are diving into the technology[3]. Aside from that, as software and hardware improve, we would see the beginning of IoT. And those IoT devices need to communicate and synchronize with each other. But in situations where more than thousands or tens of thousands of IoT devices connected, we expect that using current model of server-client may have some limitations and issues while in synchronization. So, we propose using blockchain to build IoT system. Using blockchain, we can control and configure IoT devices. We manage keys using RSA public key cryptosystems where public keys are stored in Ethereum and private keys are saved on individual devices. Specifically, we choose Ethereum as our blockchain platform because using its smart contract, we can write our own Turing-complete code to run on top of Ethereum. Thus, we can easily manage configuration of IoT devices and build key management system. Even though we can simply use account as a key management system, which most of blockchain platform supports, we decide to use Ethereum because we can manage the system in a more fine-grained way. For the proof of a concept, we use a few IoT devices instead of a full system of IoT system, which consists of thousands of IoT devices. But in our later study, we would like to build a fully scaled IoT system using blockchain.

The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.

Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions

The interconnected system consisting of tangible items featuring the capacity to transmit information is called the Internet of Things (IoT). The design encompasses various gadgets, including industrial machinery and electrical appliances. IoT devices are unprotected from safety hazards; eventually, it is important to remain vigilant about this provision to preserve data and establish that IoT systems can withstand new hacking attempts. The research seeks to provide a novel approach for machine learning (ML)--based danger indicator identification in IoT environments. Thus, a Binary Chimpanzee-Optimized Fine-Tuned Linear Least Squares Twin Support Vector Machine (BC-FTLLS-TSVM) method is introduced for detecting attack signs in IoT settings. To improve the efficiency of the proposed threat identification mechanism, an N-BaIoT dataset that accommodates the unpredictability of damaging attacks against IoT gadgets is utilized. The method includes the BCOA to feed the selection of features and greater parameters using the FTLLS-TSVM, to advance the accuracy of monitoring IoT threat signs. The proposed framework is performed by applying Python programming. Various metrics, such as Accuracy (97.50%), Precision (97.85%), F1 Score (98.35%), and False Positive Rate (FPR) (2.5%), were evaluated during the validation phase to evaluate the proposed BC-FTLLS-TSVM method. The results of the experiment show that the BC-FTLLS-TSVM paradigm procures improved classification of hazard signs in an IoT environment compared to other existing techniques. By leveraging optimized feature selection and fine-tuning through BCOA and FTLLS-TSVM, the proposed method creates effective detection of security threats. The results emphasize the ability of this approach to increase the defense and IoT systems' resistance to emerging cyber threats.

The Internet of Things (IoT) is a fast-expanding network of smart heterogeneous objects. It refers to the physical devices that are capable of communicating with other physical devices. Unlike the wireless sensor networks (WSNs), IoT is connected to worldwide Internet that exposes it to global intrusion in addition to wireless attacks inside an IoT network. It is protected by cryptographic and network security techniques, but they are vulnerable to internal and external attacks. The IoT devices are resource constrained in terms of limited storage, battery, limited transmission range and processing. To protect these tiny devices from the inside and outside cyberattacks, we need a lightweight security system that can run efficiently and effectively on these IoT devices. We also need a system, which can identify the type of intrusion and trigger an alarm in intrusion scenario to take appropriate preventive measure. Hence, an Intrusion Detection System (IDS) plays an important role to prevent such cyberattacks in IoT. These devices can be static or mobile in an IoT environment; this must be considered while designing IDS for IoT system. This book chapter presents various IDSs for an IoT system and their comparisons in terms of detection rate, false positive and accuracy in both static and mobility of devices.