Internet Of Things Cybersecurity Research Articles

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.

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.

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.

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.

Cybersecurity Risk Analysis in the IoT: A Systematic Review

The Internet of Things (IoT) is increasingly becoming a part of our daily lives, raising significant concerns about future cybersecurity risks and the need for reliable solutions. This study conducts a comprehensive systematic literature review to examine the various challenges and attacks threatening IoT cybersecurity, as well as the proposed frameworks and solutions. Furthermore, it explores emerging trends and identifies existing gaps in this domain. The study’s novelty lies in its extensive exploration of machine learning techniques for detecting and countering IoT threats. It also contributes by highlighting research gaps in economic impact assessment and industrial IoT security. The systematic review analyzes 40 articles, providing valuable insights and guiding future research directions. Results show that privacy issues and cybercrimes are the primary concerns in IoT security, and artificial intelligence holds promise for future cybersecurity. However, some attacks remain inadequately addressed by existing solutions, such as confidentiality, security authentication, and data server connection attacks, necessitating further research and real-life testing of proposed remedies.

Demystifying machine learning models of massive IoT attack detection with Explainable AI for sustainable and secure future smart cities

Smart cities rely heavily on Internet of Things (IoT) technology, which enables automation services through interconnected IoT devices. However, the widespread use of IoT applications in smart cities has resulted in security and privacy concerns that must be addressed to protect sensitive data. To safeguard smart cities from cyber attacks, learning theory-based automated attack-detection methods must be adopted. Various techniques have been proposed in the literature to create effective models for identifying IoT attacks. However, the majority of IoT detection algorithms have focused on only a few types of IoT attacks, and most IoT threat detection systems have used black-box deep learning models that lack interpretability to support their forecasts. This research aims to detect several types of large-scale attacks on IoT devices using the Extreme Gradient Boosting (XG-Boost) classifier and Explainable Artificial Intelligence (XAI) approaches. The proposed method not only improves the model’s performance but also increases trust in the model. The results of the experimental study on the IOTD20 dataset and XAI evaluation of each feature’s contribution to the model demonstrate that the proposed model can efficiently identify malicious attacks and threats, reducing IoT cybersecurity threats in smart cities.

Instantiation and Implementation of HEAD Metamodel in an Industrial Environment: Non-IoT and IoT Case Studies

Access to resources can take many forms: digital access via an onsite network, through an external site, website, etc., or physical access to labs, machines, information repositories, etc. Whether access to resources is digital or physical, it must be allowed, denied, revoked, or disabled using robust and coherent access control (AC) models. What makes the process of AC more complicated is the emergence of digital transformation technologies and pervasive systems such as the internet of things (IoT) and industry 4.0 systems, especially with the growing demand for transparency in users’ interaction with various applications and services. Controlling access and ensuring security and cybersecurity in IoT and industry 4.0 environments is a challenging task. This is due to the increasing distribution of resources and the massive presence of cyber-threats and cyber-attacks. To ensure the security and privacy of users in industry sectors, we need an advanced AC metamodel that defines all the required components and attributes to derive various instances of AC models and follow the new and increasing demand for AC requirements due to continuous technology upgrades. Due to the several limitations in the existing metamodels and their inability to answer the current AC requirements, we have developed a Hierarchical, Extensible, Advanced, Dynamic (HEAD) AC metamodel with significant features that overcome the existing metamodels’ limitations. In this paper, the HEAD metamodel is employed to specify the needed AC policies for two case studies inspired by the computing environment of Institut Technologique de Maintenance Industrielle (ITMI)-Sept-Îles, QC, Canada; the first is for ITMI’s local (non-IoT) environment and the second for ITMI’s IoT environment. For each case study, the required AC model is derived using the domain-specific language (DSL) of HEAD metamodel, then Xtend notation (an expressive dialect of Java) is utilized to generate the needed Java code which represents the concrete instance of the derived AC model. At the system level, to get the needed AC rules, Cypher statements are generated and then injected into the Neo4j database to represent the Next Generation Access Control (NGAC) policy as a graph. NGAC framework is used as an enforcement point for the rules generated by each case study. The results show that the HEAD metamodel can be adapted and integrated into various local and distributed environments. It can serve as a unified framework, answer current AC requirements and follow policy upgrades. To demonstrate that the HEAD metamodel can be implemented on other platforms, we implement an administrator panel using VB.NET and SQL.

CMTSNN: A Deep Learning Model for Multiclassification of Abnormal and Encrypted Traffic of Internet of Things

With the increasing types and number of IoT devices and malicious programs and the popularization of encryption technology in the communication process between the internet and the Internet of Things (IoT), a large amount of encrypted abnormal traffic among devices endangers IoT cybersecurity. How to identify abnormal encrypted traffic of the IoT has become the premise of cybersecurity. Presently, most of the detection methods for traffic in the IoT have problems such as simple dataset processing, imperfect feature extraction, data imbalance, and low multi-classification accuracy. In this paper, we propose a multi-classification deep learning model named the cost matrix time-space neural network (CMTSNN) for abnormal and encrypted IoT traffic. The CMTSNN is divided into three parts. The first part is the preprocessing stage of the dataset, which needs to retain the timing relation between two data packets in the stream and create a cost penalty matrix according to the sample distribution. Aimed at the robustness of feature extraction in network flow, the second part extracts time series features and then space features to ensure the robustness of feature extraction. The third part is aimed at the problem of data imbalance. The cost penalty matrix is applied to the cost penalty layer in the training process, and then the improved cross-entropy loss function is used to calculate the loss to improve the classification accuracy of minority categories and increase the overall multi-classification performance of the model. Experiments were carried out with the ToN-IoT, BoT-IoT and ISCX VPN-NonVPN datasets. Compared with current methods, the proposed method shows better performances, including accuracy, precision, recall, F1 Score and false alarm rate.

Maintaining cyberhygiene in the Internet of Things (IoT): An expert consensus study of requisite user behaviours

The Internet of Things (IoT) connects computing devices embedded in everyday objects via the internet, enabling them to send and receive data. Little is known about behaviours required to protect IoT users. The study sought to develop expert consensus on the key protective behaviours, risk behaviours, and threats for IoT cybersecurity. An online, three-round Delphi consensus study was conducted with IoT experts. In Round One, experts’ responses to open-ended questions were analysed using inductive and content analyses to categorise them into behavioural categories. In Round Two, experts rated the importance of protective behaviours, and the likelihood that risk behaviours and threats would lead to IoT breaches. In Round Three experts re-evaluated their responses based on their own and the group’s responses. Experts agreed that 28 protective behaviours, one risk behaviour, and six threats were critical for IoT cyberhygiene. Five of the top 10 protective behaviours for conventional computing were also deemed important for IoT, i.e. ‘_Limit sharing of your personal information with devices’_,_ ‘Keep your IoT devices updated’_;_ ‘Read articles about IoT security, safety and privacy risks’, ‘Use a strong firewall’, and ‘Use strong passwords on devices, networks and services_’. The study provided information on the key behaviours and threats for IoT settings, and the extent to which recommendations for conventional computing settings may also be suitable for IoT settings. These findings can inform the development of tailored behaviour change interventions to improve cybersecurity.

Code Integrity and Confidentiality: An Active Data Approach for Active and Healthy Ageing.

Internet of Things cybersecurity is gaining attention as the number of devices installed in IoT environments is exponentially increasing while the number of attacks successfully addressed to these devices are also proliferating. Security concerns have, however, been mainly addressed to service availability and information integrity and confidentiality. Code integrity, on the other hand, is not receiving proper attention, mainly because of the limited resources of these devices, thus preventing the implementation of advanced protection mechanisms. This situation calls for further research on how traditional mechanisms for code integrity can be adapted to IoT devices. This work presents a mechanism for code integrity in IoT devices based on a virtual-machine approach. A proof-of-concept virtual machine is presented, specially designed for providing code integrity during firmware updates. The proposed approach has been experimentally validated in terms of resource consumption among the most-widespread micro-controller units. The obtained results demonstrate the feasibility of this robust mechanism for code integrity.

Efficient intrusion detection toward IoT networks using cloud–edge collaboration

The Internet of Things (IoT) is increasingly utilized in daily life and industrial production, particularly in critical infrastructures. IoT cybersecurity has an effect on people’s safety, national security, and economic growth. However, the traditional cloud-based centralized intrusion detection methods cannot satisfy the demands for data privacy, network load, and timely response. In this article, we proposed an efficient intrusion detection method based on cloud–edge collaboration. The approach can reduce computational workload to speed up model training, prevent data privacy leakage, enrich training data, and detect attacks unknown to local edge devices. Specifically, stacked sparse autoencoder (SSAE) is first utilized for data dimensionality reduction to overcome the bottleneck of resource constraints on edge devices. Second, considering the long-term serial characteristics of IoT traffic data, the temporal convolutional network (TCN) model is employed to detect attack; Finally, the cloud–edge collaboration architecture based on federated learning is used to coordinate multi-party training intrusion detection models. It fills the gap of missed detection by intrusion detection models due to data silos. Experiments show that our method reduced the training time, storage and memory required for the model training process by more than 50%, respectively, but the detection accuracy is close to centralized trained models. The model trained based on cloud–edge collaboration can identify attacks unknown to local edge devices.

A Paradigm Shift in IoT Cyber-Security: A Systematic Review

Abstract: Internet of Things (IoT) describes physical gadgets with sensors, processing power, software and other technologies that connect with other devices and exchange data over the internet. Consumers and businesses everywhere are getting used to the notion that critical data processing and analysis are now being fulfilled by a new-age technology infrastructure. The Internet of Things (IoT) is the key driver of this trend, with 64 billion IoT devices estimated around the world by 2026. IoT activities span everything from smart homes and smart cities to fitness monitoring and inventory control. This rapid growth of IoT has its own set of challenges and the first one being the threat of cybersecurity. As the Internet of Things rapidly grows, cyber threats and associated risks continue to evolve and become increasingly complex. The biggest threat to these IoT devices, which has grown exponentially in the past few years, is the Distributed Denial of Service (DDoS). This highly daunting cyber-threat is extremely dangerous because perpetrators can now use IoT devices to make the attacks more severe. The formation of botnet clusters (hijacked IoT devices) is the heart of a large-scale DDoS cyber-attack. This research work focuses on the emerging trends associated with the volume of cyber-attacks. Furthermore, the research delves into the possible solutions to tackle, and possibly eradicate the cyber-threats present in the IoT systems. The domain of Machine Learning is investigated thoroughly in order to extract the ongoing research and possible solutions pertaining to the cyber-security of IoT devices and systems.

Intelligent Solutions for Manipulating Purchasing Decisions of Customers Using Internet of Things during Covid-19 Pandemic

It is a well-known fact that consumers may gain significant benefits from the effective use of IoT in pandemic and post-pandemic settings. Security vulnerabilities can be seen in the ever-increasing Internet of Things (IoT) ecosystem from cloud to edge, which is crucial to note in this particular circumstance. Most merchants, even luxury stores, have failed to implement robust IoT cyber security procedures. Therefore, the researchers sought to put forth secondary research methodologies to bring forward efficient scrutiny regarding this particular issue to properly comprehend the influence of IoT in various devices, including a smartwatch, power displaying metre, brilliant weight showing gadgets and many more. The secondary research approach allowed the researchers to collect a large quantity of data quickly, acquiring a wide range of possible solutions for security and privacy issues in Consumer IoT (CIoT) devices. Secondary research also will enable scholars to compare and contrast several papers' philosophies and research findings to get a quick conclusion. To gather information, the researchers used publications and the internet efficiently. In this situation, it helped to save a significant amount of time. Findings suggested that vulnerabilities occur in smart IoT gadgets, including the intelligent power consumption metre and brilliant weight displaying widget, due to their low-standard and conventional security system. Thus, this paper has suggested possible solutions to protect IoT devices against phishing and theft attacks.

The IoT and the new EU cybersecurity regulatory landscape

ABSTRACT This article aims to cast light on how the fast-evolving European cybersecurity regulatory framework would impact the Internet of Things (IoT) domain. The legal analysis investigates whether and to what extent existing and proposed sectoral EU legislation addresses the manifold challenges in securing IoT and its supply chain. It firstly takes into account the Cybersecurity Act, being the most recent and relevant EU legal act covering ICT products and cybersecurity services. Then, EU product legislation is scrutinised. The analysis focuses on the delegated act recently adopted by the Commission under the Radio Equipment Directive (RED), strengthening wireless devices’ cybersecurity, the Medical Devices Regulation, the Proposal for a General Product Safety Regulation and the Proposal for a Machinery Regulation. Lastly, the proposal for a revised Network and Information Systems Directive (NIS2) is assessed in terms of its potential impact on the field of IoT cybersecurity. Against this backdrop, the article concludes by advocating the need for a separate horizontal legislation on cybersecurity for connected products. To avoid fragmentation of the EU's Single Market, a horizontal legal act should be based on the principles of the New Legislative Framework, with ex-ante and ex-post cybersecurity requirements for all IoT sectors and products categories.

Intelligent Interaction Honeypots for Threat Hunting within the Internet of Things

As the Internet of Things (IoT) grows exponentially, security is falling farther and farther behind. Several new initiatives show promise for expanding the privacy and security around these devices in the future. But what about the billions of devices already out there in the wild? Security researchers are responsible for developing the tools and procedures for discovering these devices quickly, understanding the risks they bring with them, and developing tools to mitigate those risks to more manageable levels. Honeypots and honeynets have traditionally supported this work in traditional IT. However, the challenges faced by the highly distributed, incredibly heterogeneous Internet of Things make deploying such tools difficult and costly. Recent research in honeypot architectures explicitly designed for the chaotic nature of the IoT ecosystem brings a new sense of hope that may lead to significant improvements in IoT security. There is still much work to do, but research continues. IoT cybersecurity experts and threat hunters are developing strategies for securing this new frontier of technology. This study will lay the foundations for an intelligent and highly interactive honeypot solution that can scale with the researchers' requirements, providing a much-needed framework for deploying targeted IoT honeypots.

Time Series Network Data Enabling Distributed Intelligence—A Holistic IoT Security Platform Solution

The Internet of Things (IoT) encompasses multiple fast-emerging technologies controlling and connecting millions of new devices every day in several application domains. The increased number of interconnected IoT devices, their limited computational power, and the evolving sophistication of cyber security threats, results in increased security challenges for the IoT ecosystem. The diversity of IoT devices, and the variety of QoS requirements among several domains of IoT application, impose considerable challenges in designing and implementing a robust IoT security solution. The aim of this paper is to present an efficient, robust, and easy-to-use system, for IoT cyber security operators. Following a by-design security approach, the proposed system is a platform comprising four distinct yet cooperating components; a distributed AI-enhanced detection of potential threats and anomalies mechanisms, an AI-based generation of effective mitigation strategies according to the severity of detected threats, a system for the verification of SDN routing decisions along with network- and resource-related policies, and a comprehensive and intuitive security status visualization and analysis. The distributed anomaly detection scheme implementing multiple AI-powered agents is deployed across the IoT network nodes aiming to efficiently monitor the entire network infrastructure. Network traffic data are fed to the AI agents, which process consecutive traffic samples from the network in a time series analysis manner, where consecutive time windows framing the traffic of the surrounding nodes are processed by a graph neural network algorithm. Any detected anomalies are handled by a mitigation engine employing a distributed neural network algorithm, which exploits the recorded anomalous events and deploys appropriate responses for optimal threat mitigation. The implemented platform also includes the hypothesis testing module, and a multi-objective optimization tool for the quick verification of routing decisions. The system incorporates visualization and analytics functionality and a customizable user interface.

A tree-based stacking ensemble technique with feature selection for network intrusion detection

Several studies have used machine learning algorithms to develop intrusion systems (IDS), which differentiate anomalous behaviours from the normal activities of network systems. Due to the ease of automated data collection and subsequently an increased size of collected data on network traffic and activities, the complexity of intrusion analysis is increasing exponentially. A particular issue, due to statistical and computation limitations, a single classifier may not perform well for large scale data as existent in modern IDS contexts. Ensemble methods have been explored in literature in such big data contexts. Although more complicated and requiring additional computation, literature has a note that ensemble methods can result in better accuracy than single classifiers in different large scale data classification contexts, and it is interesting to explore how ensemble approaches can perform in IDS. In this research, we introduce a tree-based stacking ensemble technique (SET) and test the effectiveness of the proposed model on two intrusion datasets (NSL-KDD and UNSW-NB15). We further enhance incorporate feature selection techniques to select the best relevant features with the proposed SET. A comprehensive performance analysis shows that our proposed model can better identify the normal and anomaly traffic in network than other existing IDS models. This implies the potentials of our proposed system for cybersecurity in Internet of Things (IoT) and large scale networks.