Internet Of Things Cybersecurity Research Articles

A Survey on Cybersecurity in IoT

The proliferation of the Internet of Things (IoT) has transformed the digital landscape, enabling a vast array of interconnected devices to communicate and share data seamlessly. However, the rapid expansion of IoT networks has also introduced significant cybersecurity challenges. This paper presents a comprehensive survey of cybersecurity in the IoT ecosystem, examining the current state of research, identifying critical security vulnerabilities, and exploring advanced strategies for mitigating threats. The survey covers various facets of IoT security, including device authentication, data integrity, privacy, network security, and the emerging role of artificial intelligence (AI) in bolstering cybersecurity defenses. By synthesizing existing research and highlighting ongoing challenges, this survey aims to provide a holistic understanding of IoT cybersecurity and to guide future research endeavors.

Adaptive learning-based hybrid recommender system for deception in Internet of Thing

In the rapidly evolving Internet of Things (IoT) security domain, device vulnerabilities pose significant risks, frequently exploited by cyberattackers. Traditional reactive security measures like patching often fall short against advanced threats. This paper introduces a proactive deception system enhanced by an innovative Adaptive Learning-based Hybrid Recommender System (AL-HRS), utilizing the vulnerability and attack repository for IoT (VARIoT) database. This advanced system identifies existing vulnerabilities and dynamically recommends additional deceptive vulnerabilities based on real-time analysis of attacker behavior and historical exploit data. These recommended vulnerabilities mislead attackers into engaging with controlled environments such as honeypots, effectively neutralizing potential threats. The AL-HRS combines the predictive strengths of content-based filtering (CBF) and collaborative filtering (CF) with an adaptive learning mechanism that adjusts recommendations based on ongoing attacker interactions, ensuring the system’s efficacy amidst changing attack patterns. Our approach innovatively combines these methodologies to provide a continuously evolving security strategy, significantly enhancing the deception capability of IoT systems. Initial evaluations demonstrate a potential reduction in device compromise, highlighting the effectiveness and strategic relevance of this adaptive deception framework in IoT cybersecurity.

Cybersecurity Testbeds for IoT: A Systematic Literature Review and Taxonomy

Researchers across the globe are carrying out numerous experiments related to cybersecurity, such as botnet dispersion, intrusion detection systems powered by machine learning, and others, to explore these topics in many different contexts and environmental settings. One current research topic is the behavior of Internet of Things (IoT) devices, as they increasingly become a common feature of homes, offices, and companies.. Network testing environments which are designated as testbeds, are boosting the effectiveness of network research. However, exploratory studies in IoT cybersecurity may include a wide range of requirements. This article seeks to carry out a survey of IoT cybersecurity testbeds. A critical systematic literature review was conducted to select relevant articles, by applying a novel taxonomy to classify the testbeds. The surveyed testbeds are classified in terms of their primary target domain and other features such as fidelity, heterogeneity, scalability, security, reproducibility, flexibility, and measurability. Furthermore, we have compared the testbeds with regard to each feature. Thus, the main contribution made by this study lies in a) the insights it provides into the state-of-the-art in IoT cybersecurity testbeds, and b) the emphasis laid on the main benefits and limitations that were found in the surveyed testbeds.

Strong reasons make strong actions: What Shakespeare’s ‘King John’ can teach us about the Internet of Things

The rapid proliferation of Internet of Things (IoT) devices in modern societies brings forth unprecedented opportunities for convenience and connectivity but also poses significant cyber security challenges. This paper examines the risks associated with these devices and the regulatory frameworks governing them in key regions including the US, the EU, China and India. Through a comprehensive analysis, it becomes evident that while efforts are being made to address IoT cyber security concerns, discrepancies in approaches and regulations hinder global harmonisation and create obstacles for industry compliance. Drawing from insights into existing cyber security frameworks and industry practices, the paper proposes actionable recommendations to enhance consumer IoT cyber security. These recommendations include defining baseline security requirements, promoting expertise within IoT workforces, advocating for the independent involvement of conformity assessment bodies (CABs), leveraging the quality infrastructure ecosystem, and launching an international awareness campaign. By implementing these measures, stakeholders can foster a safer and more secure IoT environment, mitigating the risks posed by cyber threats and ensuring the trust and resilience of connected devices. As society continues to navigate the complexities of IoT adoption, it is imperative to recognise the urgency of addressing cyber security challenges. By heeding the lessons from Shakespeare’s ‘King John’ — ‘Strong reasons make strong actions’ — and taking decisive steps to fortify IoT cyber security, we can safeguard individuals, businesses and critical infrastructure from the evolving threat landscape.

Enhancing IoT network defense: advanced intrusion detection via ensemble learning techniques

The Internet of Things (IoT) has evolved significantly, automating daily activities by connecting numerous devices. However, this growth has increased cybersecurity threats, compromising data integrity. To address this, intrusion detection systems (IDSs) have been developed, mainly using predefined attack patterns. With rising cyber-attacks, improving IDS effectiveness is crucial, and machine learning is a key solution. This research enhances IDS capabilities by introducing binary attack identification and multiclass attack categorization for IoT traffic, aiming to improve IDS performance. Our framework uses the ‘BoT-IoT’ and ‘TON-IoT’ datasets, which include various IoT network traffic and cyber-attack scenarios, such as DDoS and data infiltration, to train machine learning and ensemble models. Specifically, it combines three machine learning models-decision tree, resilient backpropagation (RProp) multilayer perceptron (MLP), and logistic regression-into ensemble methods like voting and stacking to improve prediction accuracy and reduce detection errors. These ensemble classifiers outperform individual models, demonstrating the benefit of diverse learning techniques. Our framework achieves high accuracy, with 99.99% for binary classification on the BoT-IoT dataset and 97.31% on the ToN-IoT dataset. For multiclass classification, it achieves 99.99% on BoT-IoT and 96.32% on ToN-IoT, significantly enhancing IDS effectiveness against IoT cybersecurity threats.

Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow

ABSTRACT The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.

On the Contents and Utility of IoT Cybersecurity Guidelines

Cybersecurity concerns of Internet of Things (IoT) devices and infrastructure are growing each year. In response, organizations worldwide have published IoT security guidelines to protect their citizens and customers by providing recommendations on the development and operation of IoT systems. While these guidelines are being adopted, e.g. by US federal contractors, their content and merits have not been critically examined. Specifically, we do not know what topics and recommendations they cover and their effectiveness at preventing real-world IoT failures. In this paper, we address these gaps through a qualitative study of guidelines. We collect 142 IoT cybersecurity guidelines and sample them for recommendations until reaching saturation at 25 guidelines. From the resulting 958 unique recommendations, we iteratively develop a hierarchical taxonomy following grounded theory coding principles and study the guidelines’ comprehensiveness. In addition, we evaluate the actionability and specificity of each recommendation and match recommendations to CVEs and security failures in the news they can prevent. We report that: (1) Each guideline has gaps in its topic coverage and comprehensiveness; (2) 87.2% recommendations are actionable and 38.7% recommendations can prevent specific threats; and (3) although the union of the guidelines mitigates all 17 of the failures from our news stories corpus, 21% of the CVEs evade the guidelines. In summary, we report shortcomings in each guideline’s depth and breadth, but as a whole they address major security issues.

A methodical examination of single and multi-attacker flood attacks using RPL-based approaches

The Internet of Things (IoT) constitutes an intricate network of physical entities, ranging from vehicles to home appliances, each ingrained with electronics, software, sensors, and internet connectivity to facilitate data exchange and collection. This novel realm of interconnectivity, while yielding considerable advantages, also invites concerns over cybersecurity, as the vast amount of sensitive data gathered by IoT devices necessitates safeguarding against potential cyber breaches. In this context, the focus of cybersecurity in IoT involves the deployment of diverse technologies, standards, and optimum practices including, but not limited to, encryption, firewalls, and multi-factor authentication. Hence, while IoT contributes significantly to societal advancement, addressing the associated security concerns remains an imperative task. This study therefore delves into an examination of flood attacks, a prevalent form of cyber assault aimed at IoT devices. The study explored the ramifications of such an attack on an IoT system by analyzing network traffic in scenarios of singular and multiple attackers. A benchmark model devoid of an attack was employed for comparative purposes. To circumvent additional stress on the operational system, network packets were mirrored through cloud infrastructure and subsequently relayed to artificial intelligence (AI) and forensic analysis tools for real-time examination. To assure the integral cybersecurity component of continuity within IoT systems, the attacking entities were identified through AI, and forensic tools were employed to conduct real-time data analysis, thereby enabling continuous network monitoring. This study introduces an innovative approach to detecting flood attacks on IoT systems by leveraging a novel AI-based technique that integrates the ’6LoWPAN.Pattern’ feature, previously unexplored in this context. Extensive simulations were conducted to analyze the impact of flood attacks using both single and multiple-attacker scenarios. Our method demonstrated a 99.9% success rate in attacker identification, distinguishing it from existing techniques. This research contributes to the cybersecurity field by enhancing the detection capabilities within IoT networks and offering a robust model against a prevalent form of cyber threats.

Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.

Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).

Tides of Blockchain in IoT Cybersecurity.

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.

Cybersecurity in the Internet of Things (IoT) Era: Safeguarding Connected Systems and Data

In the era of the Internet of Things (IoT), where devices are interconnected at an unprecedented scale, ensuring cybersecurity has become paramount. This journal article explores the challenges and strategies associated with safeguarding connected systems and data in the IoT landscape. The study delves into the unique cybersecurity risks posed by the proliferation of IoT devices and analyzes the evolving threat landscape. The article begins by elucidating the significance of IoT in contemporary society, emphasizing its transformative impact on various sectors. Subsequently, it identifies the vulnerabilities inherent in interconnected systems, ranging from privacy concerns to potential disruptions caused by cyber-attacks. A comprehensive review of current cybersecurity frameworks and protocols follows, assessing their efficacy in addressing the dynamic threats faced by IoT ecosystems. The study highlights the importance of encryption, authentication, and intrusion detection in fortifying IoT networks against cyber threats. Case studies and real-world examples are employed to illustrate the practical implications of cybersecurity vulnerabilities in IoT devices. Furthermore, the article examines emerging technologies and innovative approaches aimed at enhancing the resilience of connected systems. The findings underscore the need for a holistic cybersecurity approach that integrates technological advancements with robust policies and user awareness. Recommendations include the adoption of standardized security measures, continuous monitoring, and collaborative efforts among stakeholders to mitigate IoT-related risks. In conclusion, this article contributes to the discourse on IoT cybersecurity, offering insights into the evolving nature of threats and effective countermeasures. As society becomes increasingly reliant on interconnected devices, ensuring the security of IoT ecosystems is imperative for fostering a trustworthy and resilient digital environment.

Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems

Securing Internet of Things (IoT)-enabled Cyber-Physical Systems (CPSs) can be challenging because security solutions intended for typical IT/OT systems may not be as effective in a CPS setting. The goal of this study is to create a mechanism for identifying and attributing two-level ensemble attacks that are specifically designed for use against Industrial Control Systems (ICSs). An original ensemble deep representation learning model is combined with decision tree algorithm to identify assaults on unbalanced ICS environments at the first level. An attack attribution network, which constitutes a collection of deep neural networks, is formed at the second level. The proposed model is tested using real-world datasets, notably those pertaining to water purification and gas pipelines. The results demonstrate that the proposed strategy outperforms other strategies with comparable computing complexity and that the recommended model outperforms the existing mechanisms.

A Blockchain Based Security system framework in Healthcare Domain using IoT

In the contemporary healthcare domain, the integration of Internet of Things (IoT) technologies has revolutionized patient care, enabling real-time monitoring, enhanced diagnostics, and personalized treatment plans. However, this digital transformation is accompanied by unprecedented challenges in ensuring the security and privacy of sensitive healthcare data. Traditional cybersecurity measures often fall short in addressing the complexity and dynamism of IoT ecosystems. This research introduces a novel blockchain-based security system framework specifically designed for the healthcare sector utilizing IoT. By leveraging the inherent security, transparency, and immutability features of blockchain technology, the proposed framework provides a robust solution to safeguard healthcare data integrity, ensure privacy, and enable secure patient data sharing among authorized entities. Through a comprehensive system architecture, this paper delineates the integration of IoT devices with a blockchain network, highlighting the deployment of smart contracts for automated access control, and data encryption mechanisms to preserve confidentiality. An empirical evaluation demonstrates the framework's effectiveness in enhancing data security while maintaining system efficiency. The discussion extends to the implications of this framework for the future of healthcare IT security, addressing potential challenges and suggesting avenues for further research. This study not only contributes to advancing the state of cybersecurity in healthcare IoT but also lays the groundwork for future innovations in the field.

A bibliometric review of Internet of Things (IoT) on cybersecurity issues

Purpose In response to the growing interest in Internet of Things (IoT) technology and its profound implications for businesses and individuals, this bibliometric study focuses on a critical yet understudied aspect, i.e. cybersecurity. As IoT adoption grows, so do concerns regarding user privacy and data security. This study aims to provide a comprehensive understanding of the current research in this vital area, shedding light on research trends, gaps and emerging themes. Design/methodology/approach The study conducted a bibliometric analysis and systematic review of literature spanning over two decades (2013–2023). Bibliometric analysis is conducted using Biblioshiny which is R-software-based advanced analytical tool. Further, VOSviewer is used to conduct network analysis. The study highlights the evolving landscape of IoT cybersecurity, emphasizing interdisciplinary intersections and the ethical dimensions of IoT technologies. Findings The study uncovers crucial concerns related to IoT adoption, emphasizing the urgent need for comprehensive cybersecurity protocols. It identifies emerging themes such as artificial intelligence and blockchain integration, indicating a shift toward interdisciplinary solutions. Furthermore, the research highlights ethical gaps in current IoT discussions, emphasizing the importance of responsible innovation. Research limitations/implications Businesses can bolster their cybersecurity strategies, policymakers can craft informed regulations and researchers are encouraged to explore IoT’s ethical dimensions. Originality/value This study pioneers a nuanced analysis of IoT cybersecurity, filling a crucial gap in the existing business and management literature. By synthesizing a decade of scholarly work, it provides foundational insights for researchers, businesses and policymakers. The research not only informs academic discourse but also offers practical guidance for enhancing IoT security measures and fostering ethical innovation.

Impact of artificial intelligence on cybersecurity: A case of internet of things

The rapid proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity challenges, necessitating innovative solutions to protect these interconnected systems. This paper explores the impact of artificial intelligence (AI) on cybersecurity within the context of IoT. By examining AI's role in enhancing threat detection, predictive analytics, and overall security protocols, this study provides a comprehensive analysis of how AI can transform IoT cybersecurity. This study highlights key findings, discusses their implications, and offers recommendations for future advancements in the field. Furthermore, the study addresses the limitations and potential risks associated with integrating AI into cybersecurity measures, including concerns related to AI-driven false positives and the evolving nature of cyber threats. By integrating case studies and real-world examples, this paper aims to bridge the gap between theoretical insights and practical applications, ultimately guiding industry professionals towards more resilient and adaptive security strategies.

Modeling the safety and reliability of IoT systems

Internet of Things (IoT) systems are developing rapidly, which leads to new opportunities, but also to new risks. The article examines the problems of reliability and security of IoT systems. Different methods for IoT reliability and cybersecurity analysis are reviewed, along with their advantages and disadvantages. Untrusted and unsafe IoT systems can lead to serious consequences 8 such as financial loss, reputational damage and physical harm. The study found that analyzing the reliability and cybersecurity of IoT systems is an important process that helps identify potential risks and problems. There are many different analysis methods, but it is important to choose the ones that suit your needs and resources. Analyzing the reliability and cyber security of IoT systems is an iterative process that requires constant monitoring and updating. It is important to consider both reliability and cybersecurity at all stages of development and operation of IoT systems. There are many tools and resources that can help you perform reliability and cybersecurity analysis of IoT systems. The article proposes one of the approaches to assessing the reliability of IoT systems and outlines the necessary solutions for the implementation of the proposed approach in practical activities.

Cybersecurity Threats in Internet of Things (IoT) Networks: Vulnerabilities and Defense Mechanisms

In fact, the mushrooming development of IoT has reshaped industries and everyday life in connecting devices, networks, and systems. Accompanying this phenomenal growth are formidable challenges related to cybersecurity. The major reasons why IoT networks are more susceptible to a variety of cyber threats are the limited computational resources of devices, a lack of standardized security protocols, and the large-scale interconnectedness of devices. This paper throws light on some of the major cybersecurity threats to IoT networks, such as device vulnerabilities, network-based attacks, and data breaches. It further pays attention to the root causes of such vulnerability exposures. Further, it discusses an in-depth analysis of some of the existing defense mechanisms involving advanced authentication methods, encryption techniques, and network security measures. The work also probes into some state-of-the-art security technologies like blockchain and artificial intelligence that hold immense promise for securing IoT. It analyzes real case studies of IoT attacks, such as the Mirai Botnet and Jeep Cherokee Hack, to depict the impact of such threats and extract some helpful lessons in securing future IoT systems. The paper then concludes with discussions on the ever-evolving IoT cybersecurity landscape and how new approaches to defense strategies continue to be thought out, developed, and implemented as these risks continue to mount.

Artificial Intelligence Strategies for Combating IoT-Centric Cyber Attacks

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors, from smart homes to industrial automation, but it has also introduced significant cyber security challenges. IoT-centric cyber attacks exploit vulnerabilities inherent in interconnected devices, posing threats to data integrity, privacy, and overall system stability. This review paper explores the evolving landscape of IoT cyber security and the role of Artificial Intelligence (AI) in mitigating these risks. We systematically analyze the types of IoT-centric cyber attacks, including Distributed Denial of Service (DDoS), man-in-the-middle (MITM), and malware injection attacks. The paper further examines AI-driven strategies for detecting, preventing, and responding to such threats. Key AI techniques such as machine learning, deep learning, and anomaly detection are evaluated for their effectiveness in real-time threat identification and adaptive defense mechanisms. Additionally, the review addresses the integration of AI with traditional cyber security frameworks and highlights emerging trends and challenges in the deployment of AI solutions. By synthesizing current research and practical applications, this paper aims to provide a comprehensive overview of AI strategies that enhance the security posture of IoT ecosystems, offering insights for researchers, practitioners, and policymakers dedicated to advancing cyber security in the IoT domain.

ENHANCING IOT SECURITY: ADDRESSING CHALLENGES, IMPLEMENTING SOLUTIONS, AND ENVISIONING CYBERSECURITY TRENDS FOR THE FUTURE

The rapid growth of the Internet of Things (IoT) has revolutionized the technology landscape, interconnecting smart devices and sensors to gather datadriven insights across various industries. Nevertheless, this transformative advancement has brought about security challenges as well, with the interconnected nature of devices leaving them vulnerable to potential cyber threats and exploitation. This research paper explores the multifaceted challenges, existing solutions, and future directions in IoT cybersecurity. It identifies hurdles like resource constraints and privacy concerns, discusses strong authentication and data encryption as solutions, and envisions AI and blockchain to enhance security. By fostering collaboration among stakeholders, a safer and more resilient IoT ecosystem can be achieved, ensuring the IoT's full potential while addressing security concerns.