Архитектурные методы аппаратных механизмов безопасности в устройствах Интернета вещей

IoT devices rely on authentication mechanisms to render secure message exchange. During data transmission, scalability, data integrity, and processing time have been considered challenging aspects for a system constituted by IoT devices. The application of physical unclonable functions (PUFs) ensures secure data transmission among the internet of things (IoT) devices in a simplified network with an efficient time-stamped agreement. This paper proposes a secure, lightweight, cost-efficient reinforcement machine learning framework (SLCR-MLF) to achieve decentralization and security, thus enabling scalability, data integrity, and optimized processing time in IoT devices. PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication. An IoT network gathers information of interest from multiple cluster members selected by the proposed framework. In addition, the software-defined secured (SDS) technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform. Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime, energy, secured data retrieval rate, and performance ratio. By enabling the proposed framework, number of residual nodes is reduced to 16%, energy consumption is reduced by up to 50%, almost 30% improvement in data retrieval rate, and network lifetime is improved by up to 1000 msec.

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.

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.

Modern embedded electronic devices are gradually becoming an essential part of everyday lives, proving indispensable for daily activities. The growth of the Internet of things (IoT) technology fuels this phenomenon. IoT devices are found everywhere around us – smartphones, smartwatches, home automation, autonomous cars, and medical implants for human bodies. These devices find widespread use in medical imaging, video surveillance as embedded vision, cloud computing platforms, and military applications. Ensuring the reliability and safety of these devices is of prime importance, since these devices store sensitive data. A wide range of such embedded devices is a common target for intellectual property (IP) theft and reverse-engineering attacks by hackers to access confidential information and counterfeit integrated circuits. Physically unclonable functions (PUFs) are innovative hardware defense techniques that promise a fundamental change in our approach to many security problems. PUFs possess desirable properties like low cost, simple architectures, a light weight, versatility, and the valuable advantage of unclonability. This unclonable property is a consequence of intrinsic random variations at the micro- and nanoscale levels in the manufacturing processes and cannot be controlled or removed. This characteristic also serves as a unique fingerprint for the integrated circuit chips, similar to humans. The widespread usage of PUF techniques in security applications makes it imperative for hardware designers to appreciate its working and understand its uses in practice fully. This chapter aims to address these and other topics by presenting a detailed introduction of the concepts, evaluation parameters, and various ramifications of physically unclonable functions. PUF techniques also provide an economical alternative to ciphers and hash functions for secure cryptographic protocols. It is also necessary to provide security of IoT devices, have economic viability, and fit well with embedded devices’ heterogeneous nature. PUF circuits receive challenges as a sequence of bits and generate matching responses called a challenge-response pair, which can be thought of as a secret key or unique fingerprint. It also obviates the need for storing the keys in nonvolatile memories with dedicated protection, which is a costly affair. In this chapter, different PUFs, including optical PUF, delay-based PUFs, weak and strong PUFs, etc., the maturity of this technology, and their various applications are investigated. This chapter also emphasizes critical challenges toward PUF utilization in IoT and related fields and significant future development areas like critical public infrastructure, reconfigurability, and bit-specific reliability. The unique IoT domain protection criteria and how PUFs can be used to fulfill them are also examined. The chapter’s emphasis is on proven and well-understood strategies involving their benefits and shortcomings.

This dissertation proposes a solution to the growing concern about the security of Internet of Things (IoT) devices in home or business environments. Entitled MAIOS (Multi Accountability IoT Oriented Switch), the system offers an SDN (Software Defined Networking) switch architecture, which provides effective segmentation for IoT devices, increasing their protection against cyber threats. The research begins by contextualizing the exponential increase in IoT devices and the security challenges associated with them. MAIOS addresses this issue through a centralized control layer, which allows efficient management of IoT devices and the implementation of dynamic security policies, which can be applied from an SDN control plane application layer. The system was designed considering functional, non-functional requirements and deployment scenarios, either in home or business environments. To increase usability, an intuitive interface was developed for users, facilitating the configuration and monitoring of IoT devices. The solution is based on open source technologies such as pfSense proxy/firewall and Home Assistant to provide functionalities such as firewall, IoT control and real-time traffic monitoring. The effectiveness of MAIOS is evaluated through security and performance tests in simulated environments, which demonstrated its ability to identify and mitigate cyber threats, providing robust security for IoT devices. This dissertation offers a contribution to the field of IoT security, presenting a practical, affordable and effective solution to protect devices in residential and business networks. MAIOS represents an important advance in ensuring digital security in an increasingly connected world.

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.

Internet of things is one of the latest technology in today’s era, IoT devices are increasing day by day and become a part of our life. Sometimes we even didn’t know that we are using and surrounded by such technolgy. Smart devices like smart phone, tablets and any wearables are the example of IoT technology. These all are IoT devices and there are many more. As we all are in 2020 and facing the pandemic we all are become more connected with IoT devices to be safe. In this way the number of IoT devices per person will increase so much rapidly and several new innovations either of hardware or software take place. But the Internet of Things (IoT) devices are becoming more popular, the security measures are not upto the mark, and several issues will come in knowledge at different time on different devices. So we have to think about the security and privacy of user and should make it correct. We should learn from the past issues and try to fix them accordingly. To overcome the issues of IoT devices we can have to ways, one is to use ECC which will enchance the security in IoT device and another possible solution can be Blockchain. In this paper we will discuss about ECC and blockchain technology both and try to find a comparitive study.

Seeing is not always believing: Insights on IoT manufacturing from firmware composition analysis and vendor survey

A Physical Unclonable Function (PUF) is a physical system that leverages manufacturing process variations to generate unclonable and inherent instance-specific measurements of physical objects. PUF is equivalent to human biometrics in many ways where each human has a unique fingerprint. PUF can securely generate unique and unclonable signatures that allow PUF to bootstrap the implementation of various physical security issues. In this thesis, we discuss PUFs, extend it to a novel SW-PUF, and explore some techniques to utilize it in security applications. We first present the "SW-PUF" - basic building block of this thesis, a novel PUF design that measures processor chip ALU silicon biometrics in a manner similar to all PUFs. Additionally, it composes the silicon measurement with the data-dependent delay of a particular program instruction in a way that is difficult to decompose through a mathematical model. We then implement the proposed PUF to solve various security issues for applications such as Software Protection and Trusted Computing. We prove that the SW-PUF can provide a more robust root of trust for measurement than the existing trusted platform module (TPM). Second, we present the "Reversible SW-PUF", a novel PUF design based on the SW-PUF that is capable of computing partial inputs given its outputs. Given the random output signature of specific instruction in a specific basic block of the program, only the computing platform that originally computed the instruction can accurately regenerate the inputs of the instruction correctly within a certain number of bits. We then implement the Reversible SW-PUF to provide a verifiable computation method. Our scheme links the outsourced software with the cloud-node hardware to provide proof of the computational integrity and the resultant correctness of the results with high probability. Finally, we employ the SW-PUF and the Reversible SW-PUF to provide a trust attribute for data on the Internet of Thing (IoT) systems by combining data provenance and privacy-preserving methods. In our scheme, an IoT server can ensure that the received data comes from the IoT device that owns it. In addition, the server can verify the integrity of the data by validating the provenance metadata for data creation and modification.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

This paper explores various security and privacy issues inherent in IoT devices, ranging from vulnerabilities in device firmware and software to data breaches and unauthorized access. We delve into the challenges of securing IoT devices due to their resource constraints, diverse communication protocols, and often lax security practices during development. Additionally, we discuss privacy implications stemming from the collection and sharing of sensitive personal data by IoT devices, as well as the potential for surveillance and data misuse. Furthermore, we examine the implications of IoT devices in critical infrastructure and industrial systems, where security breaches can have severe consequences. Finally, we propose potential solutions and best practices to address these challenges, including robust encryption methods, regular security updates, and improved authentication mechanisms, to ensure the security and privacy of IoT devices in an increasingly connected world. The exponential growth of IoT devices across various sectors such as smart homes, healthcare, transportation, and industrial automation underscores the importance of ensuring their security and privacy. As these devices become more integrated into daily life and critical infrastructure, any vulnerability can have widespread and severe consequences. The evaluation of alternative performances through Complex Proportionality Assessment (COPRAS) requires an understanding of key criteria, exploration of options, and comparison of relevant facts. Meeting the decision-makers' desire for comparing grades involves choosing among multiple options based on predetermined competing requirements. COPRAS offer a method for such assessments in real-world scenarios, where criteria are nuanced and values cannot be quantified numerically. From the result smart thermostat got the first rank whereas wearable fitness tracker is having the lowest rank.

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 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.

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

Billions of IoT (Internet of Things) devices are being introduced to our everyday life each year. Most of them have little to no security features embedded in them. But these devices connect to internet and communicate with other devices all the time which pose a great security and privacy risk. Due to the resource constraint nature of these devices, initially security was not being considered as an essential feature. Therefore, any security features to be included in these devices should be minimal in area, energy consumption and added delay. Physical unclonable function (PUF) has emerged as a low-overhead solution for a variety of security concerns in recent years. In this paper, we have presented a simple security method for small IoT devices based on PUFs. The idea is to secure the backup data during sleep mode of an embedded processor or when power is unavailable for a batteryless system. With the emergence of nano-technology, memristors are being widely explored due to their non-volatility, low footprint among other advantages. Our proposed security system utilizes a memristor based PUF along with memristors as a non-volatile backup memory. The proposed system is very lightweight as this domain demands and provide reasonable security.