Security Of Internet Of Things Devices Research Articles

A systematic review on elliptic curve cryptography algorithm for internet of things: Categorization, application areas, and security

Cyberattacks on the Internet of Things (IoT) may be avoided by implementing cybersecurity protocols on IoT devices, platform packages, and Internet tools. A significant problem is developing a simple security mechanism suitable for resource-constrained IoT devices. Elliptic curve cryptography (ECC) is extensively utilized and well-suited for use in low-resource environments like smartphone devices. ECC utilizes short crypto keys with the same security strength as conventional cryptosystems, decreasing power consumption and enhancing device efficiency. This study comprehensively evaluates ECC data privacy and security techniques in IoT-based devices. The research also looked into other cryptography protocols that ECC may use to increase security and performance. The inclusion procedures were papers published between 2018 and 2023, written in English, and published in a peer-reviewed scholarly outlet. This research eventually reviewed 61 suitable publications in total. Compared to other cryptographic methods, the study discovered that ECC has received greater attention or is frequently employed in IoT-based systems. However, additional issues and concerns must be addressed, including efficient IoT device security, maintaining privacy, and device security against vulnerability assaults such as man-in-the-middle attacks. For the efficient security of the IoT-based system, an improved ECC-based algorithm should be developed to enhance the security and privacy issues of the IoT system.

Securing the Internet of Things: A Comprehensive Review of Security Challenges and Artificial Intelligence Solutions

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.

IoT Security Challenges and Solutions for Data at Rest: A Systematic Literature Review

The rapid expansion of the Internet of Things (IoT) has significantly transformed both consumer and industrial domains, driving the urgent need for robust security measures to protect data at rest. SLR investigates into the challenges associated with securing IoT devices and data, exploring the limitations of existing security frameworks and the intricate requirements imposed by global data protection regulations such as GDPR. The researcher review current approaches, including privacy-by-design principles and the deployment of symmetrical data protection frameworks, as highlighted in recent studies. Through a comprehensive analysis of literature and existing technologies, we identify critical gaps in the protection strategies and propose enhanced methods for ensuring data security and privacy in IoT systems. The findings emphasize the role of developers in integrating privacy considerations early in the development process and the impact of regulatory complexities on the practical implementation of data protection measures. Furthermore, the paper evaluates innovative security solutions, such as full stack security architectures and adversarial training models, assessing their effectiveness in real-world applications. This study aims to provide a deeper understanding of the IoT security landscape and to suggest actionable strategies for improving data protection practices across the IoT ecosystem

Security paradigms for IoT in telecom networks: Conceptual challenges and solution pathways

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: IoT, Telecom, Networks, Security.

Efficient implementations of CRAFT cipher for Internet of Things

The rapid growth of the Internet of Things (IoT) highlights the importance of lightweight cryptography in maintaining security. However, enhancing performance while ensuring the same level of security remains a significant challenge. This paper presents two innovative architectures for the CRAFT lightweight block cipher, aiming to enhance performance without compromising security. The novel Serial and Unrolled architectures are introduced to achieve low area usage and high throughput, respectively. Specifically, the Serial architecture reduces the datapath from 64-bit to 4-bit, significantly decreasing the area. The Unrolled architecture, on the other hand, minimizes latency from 32 to 16. Additionally, Boolean satisfiability (SAT) solvers are employed to identify a lower-cost area implementation of the S-Box. The proposed designs underwent evaluation on three distinct FPGA platforms: Artix-7, Kintex-7, and Spartan-7. The results show that the low area design reduces area usage by 10.16% compared to the previous design. Additionally, the S-Box implementation achieves a significant area reduction of 28.9%. On the other hand, the unrolled design enhances the maximum throughput by 40.53% compared to the previous design. Therefore, the proposed designs could offer enhanced performance while maintaining security for IoT devices.

Enhancing IoT data confidentiality and energy efficiency through decision tree-based self-management

Given the widespread deployment of IoT devices characterized by heterogeneity and energy limits, a greater emphasis on IoT data security within IoT environments has become required. This critical situation encouraged the deployment of lightweight encryption solutions to avoid additional attacks. These solutions, which are based on traditional encryption algorithms, are intended to fulfill the demands of resource-constrained devices while maintaining a high security level. In this research, we provide a novel IoT framework that uses lightweight cryptographic ciphers and the Autonomic Computing paradigm to self-manage the security of IoT devices. Our framework is based on a decision tree technique to guarantee the IoT data confidentiality by selecting and changing in an autonomic manner the most appropriate lightweight encryption algorithm to be used in order to improve energy efficiency by extending the lifespan of IoT devices and to provide an adapted security level according to IoT application type and IoT object state.

IoT Security Framework Optimized Evaluation for Smart Grid

Modern systems' needs may be satisfied by smart grid technologies. Since we frequently struggle to effectively manage security, the smart grid's capacity is frequently underutilized. Despite the fact that a variety of solutions have been offered for securing the smart grid, the problem still exists that no single solution can entirely protect the environment. We provide a protection architecture for the IoT-connected smart grid. The proposed framework to secure IoT devices for the smart grid includes three complementary approaches. By conducting a rigorous comparative analysis of our proposed solution alongside four existing models, we contribute to the ongoing discourse on bolstering the security infrastructure of the smart grid IoT environment. Our optimized evaluation provides valuable insights into the strengths, weaknesses, and unique attributes of each model, offering a comprehensive understanding of their respective applicability and efficacy within the intricate realm of sensor-based applications. Two testing configurations were used to evaluate the Threat Mitigation Framework. It demonstrated superior performance in recognizing attacks like XSS across all testing configurations. In each of the two test sets, we also assessed the device management functions, and we found that they accurately recognized and presented IoT for the smart grid controller.

A novel trusted hardware-based scalable security framework for IoT edge devices

The Internet of Things (IoT) devices are pervasively deployed and embedded into our daily lives. Over several years, the massive assimilation of IoT devices has given rise to smart cities, smart factories, smart farms, etc. While IoTs can collect immense amounts of user and environmental information, the lack of adequate security measures is among the major concerns about information security and privacy. With the ever-increasing presence of IoTs, information flow between IoT devices in a single or multi-layered network is becoming increasingly vulnerable to attacks. Although several software-based solutions have been developed over the years that improve the security of resource-constrained IoT devices, there are limited efforts on hardware-oriented IoT security solutions. This paper proposes a Trusted HArdware-based Scalable Secure Architecture (THASSA) for IoT Edge systems. Leveraging standard cryptographic algorithms, THASSA provides a secure mechanism for data transfer and off-loads cryptography operations to trusted hardware. The modularized THASSA architecture allows devices to be added or removed without user intervention. Also, THASSA could be scaled to multiple subsystems using multiple trusted hardware to form IoT clusters with secure data path(s) for mission-critical applications. An experimental study has verified the correctness and effectiveness of THASSA.

SECURITY PARADIGMS FOR IOT IN TELECOM NETWORKS: CONCEPTUAL CHALLENGES AND SOLUTION PATHWAYS

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: Security, IoT, Telecom, Solutions, Network.

Guardians of the iot realm: a comparative analysis of cryptographic security solutions for bolstering iot device networks

The "Internet of Things" (IoT) is an emerging technology that allows electronic devices and sensors to connect with one another over the Internet. The IoT is a network of smart devices such as sensors that are connected through cables or wirelessly. Today, IoT devices are commonly used in medical science, industrial automation, and smart home automation, among other applications. Easy accessibility and the open-source environment of IoT devices are major threats to privacy and security. In this paper, we comprehensively discuss areas of IoT, including architecture, details of IoT layers with respect to security algorithms, protocols, attacks, and their mitigation. The primary purpose of this research is to provide an efficient and cryptographically proven algorithm scheme with appropriate hardware to improve IoT device network security. The presented research enhances the network security of IoT devices by presenting an algorithmic framework fortified by cryptography and supported by the finest hardware options. After evaluating several cryptographic algorithms and hardware options, a more secure solution for IoT infrastructure is proposed. The article in particular, proposes a detailed strategy that addresses security at every level of the IoT system. This research is an important step towards ensuring the integrity and security of IoT devices in a networked environment, with the primary goal of increasing the protection of sensitive data and interactions.

Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

Securing IoT devices with fast and energy efficient implementation of PRIDE and PRESENT ciphers

The rise of low-power, cost-efficient internet-connected devices has led to a need for lightweight cryptography. The lightweight block cipher PRIDE, designed by Martin R. Albrecht, is one of the most efficient ciphers designed for IoT-constrained environments. It is useful for connected devices, requires fewer resources to implement, and has high performance. PRIDE is a software-oriented lightweight cipher optimized for microcontrollers. This paper focuses on the FPGA implementation of the PRIDE cipher by keeping throughput, energy, and power consumption metrics focused. The paper also presents a novel and simpler diagrammatical view of a Matrix Layer implementation of the PRIDE cipher. We also implemented the PRESENT cipher using the same metrics. We analyzed different design metrics on Field Programmable Gate Arrays (FPGAs) and compared the metrics of the PRIDE implementation with the well-known cipher PRESENT. This gives us an insight into the efficiency and reliability of PRIDE in IoT-constrained environments. We also proposed different architectures of the PRIDE cipher for 16-bit and 32-bit datapaths.

BlockFog: A Blockchain-based Framework for Intrusion Defense in IOT Fog Computing

In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.

Systematic Literature Review on the Role of Big Data in IoT Security

The Internet of Things (IoT) is an interconnected system of physical objects that are embedded with different sensors (for receiving information), chips, software, and other techniques, which allow connecting and transferring of data to other devices via the Internet without human involvement. Since the number of IoT devices is increasing, large amounts of data are being generated from different sources in different formats. This information needs to be used effectively to gain useful insights for enhancing IoT security. Hence, big data techniques are proposed for managing the data to overcome different issues of IoT. Despite the outstanding achievements in IoT security, a systematic literature review (SLR) on implementing big data for securing IoT is lacking. The number of existing related SLRs is limited. Hence, this paper provides a systematic literature review on the use of big data for securing IoT devices. It summarizes the relevant literature produced during the last six years and provides meaningful insights gained by these existing studies. Moreover, it provides a discussion on the sources of IoT big data, the techniques and approaches of big data for securing IoT systems. Current challenges and future research directions are discussed.

Lightweight Cryptography for Internet of Things: A Review

The paper examines the rising significance of security in Internet of Things (IoT) applications and emphasizes the need for lightweight cryptographic solutions to protect IoT devices. It acknowledges the growing prevalence of IoT in various fields, where sensors collect data, and computational systems process it for action by actuators. Due to IoT devices' resource limitations and networked nature, security is a concern. The article compares different lightweight cryptographic block cipher algorithms to determine the best approach for securing IoT devices. It also discusses the merits of hardware versus software solutions and explores potential security threats, including intrusion and manipulation. Additionally, the article outlines future work involving the implementation of the trusted Advanced Standard Encryption block cipher in IoT devices, including its use in quick-response (QR) code scanning and messaging platforms. It acknowledges existing drawbacks and suggests areas for improvement in IoT system performance and security.

IOT SECURITY AND SOFTWARE TESTING

The Internet of Things (IoT) symbolizes the era of increased information exchange and interaction between devices through Internet of Things technology. However, this fascinating technology brings with it a number of security challenges. Some of the security issues stem from the nature of IoT devices. IoT devices are often designed to be cheap and uncomplicated. As a result, security tests may be neglected and security vulnerabilities may arise. There are other factors that compromise the security of IoT devices. For example, most IoT devices have standard passwords that have not been changed. Attackers can easily seize devices by manipulating them. There are data leaks from compromised devices.

Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors

Physical adversarial attacks present a novel and growing challenge in cybersecurity, especially for systems reliant on physical inputs for Deep Neural Networks (DNNs), such as those found in Internet of Things (IoT) devices. They are vulnerable to physical adversarial attacks where real-world objects or environments are manipulated to mislead DNNs, thereby threatening the operational integrity and security of IoT devices. The camera-based attacks are one of the most practical adversarial attacks, which are easy to implement and more robust than all the other attack methods, and pose a big threat to the security of IoT. This paper proposes Adversarial Camera Patch (ADCP), a novel approach that employs a single-camera patch to launch robust physical adversarial attacks against object detectors. ADCP optimizes the physical parameters of the camera patch using Particle Swarm Optimization (PSO) to identify the most adversarial configuration. The optimized camera patch is then attached to the lens to generate stealthy and robust adversarial samples physically. The effectiveness of the proposed approach is validated through ablation experiments in a digital environment, with experimental results demonstrating its effectiveness even under worst-case scenarios (minimal width, maximum transparency). Notably, ADCP exhibits higher robustness in both digital and physical domains compared to the baseline. Given the simplicity, robustness, and stealthiness of ADCP, we advocate for attention towards the ADCP framework as it offers a means to achieve streamlined, robust, and stealthy physical attacks. Our adversarial attacks pose new challenges and requirements for cybersecurity.

ILAPU-Q: An Improved Lightweight Authentication Protocol for IoT Based on U-quark Hash Function

Background: In the last decades, the development of Internet activities has been significantly accelerated, particularly with the emergence of the Internet of Things (IoT). Heterogeneous devices in the IoT can seamlessly and feasibly inter-connect with each other without human interaction. Due to this revolution, many applications have been adopted in the arena of smart healthcare, e-commerce, environmental and habitat monitoring, etc. In order to promote and facilitate people's standards of living around the world. However, these unbounded applications bring more challenges to the storage capabilities of devices, and their security and privacy preservation. Moreover, security issues suffer from weak authentication protocols. Methods: To address these issues, suitable and secure lightweight mutual authentication schemes based on Elliptic Curve Cryptography (ECC) are required for the approval of Identity Management (IDM) of devices in the IoT. In this paper, we will propose an improved mutual authentication scheme based on ECC, coupled with a relevant seminal work considered as a reference in the field. This scheme is combined with U-Quark, a lightweight hash function, to guarantee the security needed in the IoT environment. Results: We will compare our amended protocol with a seminal scheme as an established reference in terms of computation cost, storage cost, and executing CPU time to demonstrate that our version can ensure the most favorable performance during the authentication process. Conclusion: Finally, our proposed mutual authentication scheme has demonstrated its effectiveness in enhancing the security of IoT devices when compared to the seminal work in the same computational environment.

Securing IoT Devices from DDoS Attacks through Blockchain and Multi-Code Trust Framework

In an era where IoT devices are integral components of numerous systems, their security from prevalent DDoS attacks has become imperative. The traditional security protocols are unable to withstand the sophisticated nature of these attacks, presenting an escalating vulnerability issue in the network security ecosystem. This research proposes a revolutionary approach to address these challenges through a “Blockchain and Multi-Code Trust Framework” utilizing the “UNB IoT DDoS Data Set.” Leveraging the decentralized and immutable characteristics of blockchain technology, alongside a multi-code driven trust mechanism, this framework aims to create a secure, robust, and resilient environment for IoT devices. Our methodology capitalizes on blockchain's transparency to foster trust and validation in network transactions, significantly reducing the threat surface for DDoS attacks. Furthermore, the incorporation of a multi-code system intensifies the security measures, providing several layers of protection against potential breaches. Data sets used in this study encompass diverse real-world IoT network traffic, meticulously collected from the “UNB IoT DDoS Data Set,” to facilitate a comprehensive analysis of the system's performance under various attack scenarios. Our preliminary findings indicate a remarkable improvement in the security posture of IoT devices, exhibiting a substantial reduction in successful DDoS attacks, thereby achieving a new pinnacle in IoT security. This research not only proposes a robust solution to a pressing issue but also opens avenues for further innovations in IoT device security using blockchain technology.

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices