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Cyber-Physical Security in Smart Healthcare: Protecting IoT-Enabled Medical Devices from Spyware, Ransomware, and Network-Based Exploits
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Financial stability is a critical pillar of economic resilience, particularly in the face of market uncertainty driven by global disruptions, technological shifts, and evolving regulatory landscapes. Traditional risk assessment models often struggle to adapt to the increasing complexity and speed of financial markets. Artificial Intelligence (AI)-driven risk assessment frameworks offer a transformative solution by leveraging machine learning, predictive analytics, and real-time data processing to enhance financial decision-making. These AI-powered models can detect emerging risks, identify market anomalies, and optimize portfolio strategies, providing financial institutions with a proactive approach to mitigating volatility. The integration of AI in financial risk management enhances the accuracy of credit scoring, fraud detection, and liquidity analysis, reducing systemic vulnerabilities and improving investor confidence. Additionally, AI-driven sentiment analysis and natural language processing (NLP) enable financial analysts to interpret market signals more effectively, offering insights into economic trends and investment opportunities. Despite its advantages, AI adoption in financial stability assessments faces challenges such as algorithmic bias, regulatory compliance, and data privacy concerns. Addressing these limitations requires a balanced approach that incorporates ethical AI practices, transparent decision-making frameworks, and robust cybersecurity measures. This paper explores the role of AI in financial stability, focusing on its impact on market risk assessments, investment strategies, and regulatory compliance. By examining case studies of AI-driven financial decision-making, it highlights the potential of intelligent risk assessment models in mitigating market uncertainty. The findings emphasize the need for continued collaboration between policymakers, financial institutions, and AI researchers to harness technology for a more resilient and adaptive financial ecosystem.
The transition toward a decentralized energy infrastructure in the United States is critical to addressing growing concerns over grid instability, energy security, and sustainability. Traditional centralized grids face increasing vulnerabilities due to aging infrastructure, climate-induced disruptions, and rising electricity demand. Decentralized energy systems, including distributed renewable energy sources, microgrids, and energy storage solutions, offer resilience and flexibility but require substantial investment. Public-private partnerships (PPPs) have emerged as a viable mechanism to bridge financing gaps by leveraging governmental support, private sector expertise, and innovative financing models. Digital financial instruments, such as blockchain-based energy trading platforms, green bonds, and tokenized energy assets, are reshaping investment strategies by enhancing transparency, liquidity, and accessibility in the energy market. The integration of decentralized finance (DeFi) in energy investment enables peer-to-peer transactions, reducing reliance on traditional financial intermediaries and fostering community-driven energy projects. Moreover, regulatory frameworks and policy incentives play a crucial role in incentivizing private sector participation and ensuring the scalability of decentralized energy initiatives. This paper examines how the synergy between PPPs and digital financial instruments can drive investment in decentralized energy projects, addressing grid instability challenges in the U.S. By analyzing case studies of successful implementations, policy recommendations, and emerging trends in energy finance, this study highlights the transformative potential of innovative investment models in accelerating the clean energy transition. The findings underscore the necessity of a collaborative, technology-driven approach to secure a resilient, decentralized energy future.
The rapid adoption of cloud computing in the United States has revolutionized data storage, processing, and accessibility for enterprises, governments, and individuals. However, this expansion has also led to an increase in sophisticated cyber threats, particularly those leveraging artificial intelligence (AI) for adversarial attacks. Threat intelligence and predictive analytics have emerged as essential components in enhancing cloud security, enabling proactive defense mechanisms against evolving threats. By integrating real-time data analysis, machine learning models, and behavioral analytics, organizations can detect anomalies, predict cyberattacks, and mitigate risks before they escalate. Traditional cybersecurity measures rely on reactive responses, often failing to address zero-day vulnerabilities and AI-driven cyber threats. In contrast, predictive analytics leverages historical threat data, AI pattern recognition, and anomaly detection techniques to forecast potential attack vectors. The synergy between AI-driven threat intelligence and cloud security frameworks strengthens automated incident response, reducing human intervention and improving detection accuracy. However, challenges such as adversarial AI attacks, bias in threat models, and regulatory compliance issues must be addressed to ensure ethical and effective implementation. This article explores the role of threat intelligence and predictive analytics in USA cloud security, examining the benefits, challenges, and future directions of AI-driven cybersecurity frameworks. It also evaluates regulatory considerations, ethical concerns, and the integration of quantum-resistant security solutions. By leveraging AI and predictive analytics, cloud security infrastructures can proactively mitigate cyber threats, enhance resilience, and safeguard critical digital assets in an increasingly complex threat landscape.
The rapid evolution of cyber warfare, geopolitical tensions, and foreign cyber threats necessitates a comprehensive cloud-driven security strategy to safeguard U.S. national interests. As adversarial nations leverage AI-powered cyberattacks, disinformation campaigns, and supply chain vulnerabilities, securing data sovereignty, enhancing threat intelligence, and fortifying digital warfare preparedness have become national security imperatives. Traditional cybersecurity frameworks struggle to counteract state-sponsored cyber intrusions and asymmetric digital warfare tactics, requiring a shift towards cloud-native architectures, AI-enhanced cyber defenses, and Zero Trust security models. This paper explores the role of cloud innovation in strengthening national resilience, emphasizing the intersection of cloud computing, cybersecurity, and intelligence operations. By deploying sovereign cloud frameworks, the U.S. can retain full control over critical national data, reducing exposure to foreign espionage, data breaches, and jurisdictional conflicts. Additionally, integrating machine learning-driven threat intelligence platforms enables real-time cyber threat analysis, adversarial behavior prediction, and automated incident response. Digital warfare preparedness is further enhanced through AI-augmented cyber operations, cyber deterrence strategies, and the deployment of offensive cybersecurity tools. Next-generation threat modeling, behavioral analytics, and cyber deception technologies ensure the U.S. remains proactive in defending against cyber-enabled economic and military disruptions. However, challenges such as data sovereignty enforcement, cloud infrastructure security, and regulatory compliance must be addressed to maximize the effectiveness of cloud-based cyber defense strategies. This paper outlines policy recommendations, emerging technological advancements, and strategic partnerships necessary to establish a robust, cloud-secured digital warfare infrastructure that protects U.S. national sovereignty and global cyber dominance.
The construction of cleanrooms is a critical endeavor in industries such as pharmaceuticals, semiconductors, and biotechnology, where stringent contamination control, cost efficiency, and adherence to strict timelines are paramount. Traditional cleanroom construction methods often face challenges related to budget overruns, schedule delays, and contamination risks, necessitating innovative technological solutions. The integration of Building Information Modeling (BIM), Digital Twin technology, and AI-driven project management systems is transforming cleanroom construction by enhancing precision, efficiency, and risk mitigation. BIM facilitates real-time collaboration, clash detection, and resource optimization, reducing construction errors and rework, ultimately minimizing costs and project delays. Digital Twin technology, by providing a dynamic virtual representation of the construction process, enables real-time monitoring, predictive maintenance, and enhanced quality control, ensuring compliance with strict cleanroom standards. Furthermore, AI-driven project management tools leverage predictive analytics, automation, and machine learning algorithms to optimize scheduling, labor allocation, and material procurement, preventing cost escalations and streamlining workflows. This paper explores the synergistic impact of BIM, Digital Twin, and AI technologies in cleanroom construction, emphasizing how their combined application improves cost efficiency, accelerates project timelines, and mitigates contamination risks. Through case studies and performance analysis, we demonstrate the effectiveness of these technologies in revolutionizing cleanroom project execution. By adopting these cutting-edge digital solutions, stakeholders can achieve unprecedented efficiency, regulatory compliance, and contamination-free environments, ensuring the sustainable and future-proof development of critical cleanroom infrastructure.
In an increasingly interconnected world, cybercrime and data privacy challenges have escalated into critical global issues, threatening governments, organizations, and individuals alike. The proliferation of sophisticated cyberattacks, including ransomware, data breaches, and nation-state hacking, underscores the urgent need for robust cybersecurity governance. Compounding these threats are evolving regulatory landscapes and a lack of harmonized international frameworks, leaving gaps in addressing cross-border cybercrimes and ensuring data privacy. This article explores the imperative of strengthening cybersecurity policy frameworks to combat global cybercrime and safeguard sensitive data. It begins with an overview of the current cybersecurity governance landscape, highlighting gaps and inconsistencies in policy enforcement. Emphasis is placed on the integration of adaptive regulatory mechanisms, public-private partnerships, and standardized global practices to ensure a unified response to cyber threats. Key strategies discussed include the adoption of proactive risk assessment methodologies, the implementation of privacy-by-design principles, and the enhancement of international cooperation for intelligence sharing and joint cyber defense initiatives. The article also delves into case studies illustrating the effectiveness of comprehensive policy frameworks in mitigating cyber risks and fostering organizational resilience. As cyber threats continue to evolve, addressing these challenges requires a coordinated and forward-looking approach that balances innovation with security. By advancing cybersecurity governance, stakeholders can strengthen trust in digital ecosystems, safeguard privacy, and ensure the continuity of global digital operations.
This article explores the integration of automation and deep learning in modern manufacturing to address critical challenges such as redundancy, defects, vibration analysis, and material strength. As manufacturing processes evolve, the need for more sophisticated methods to optimize production efficiency and product quality becomes paramount. Automation, coupled with deep learning techniques, offers powerful tools for enhancing manufacturing processes. These technologies enable predictive maintenance, reducing downtime by identifying potential equipment failures before they occur. Furthermore, deep learning algorithms can analyse complex data sets to detect defects in products with greater accuracy and speed than traditional methods. Vibration analysis, a key aspect of predictive maintenance, benefits from automated systems that monitor and diagnose issues in real-time, preventing costly disruptions. Additionally, deep learning models can assess material strength and predict potential failures, ensuring that products meet rigorous safety and quality standards. The synergy between automation and deep learning not only streamlines manufacturing processes but also enhances the ability to adapt to changing conditions, thereby minimizing operational inefficiencies. This article highlights the transformative impact of these technologies on the manufacturing industry, illustrating their potential through case studies and practical examples. By addressing key challenges such as redundancy and defects, automation and deep learning contribute to the creation of more reliable, efficient, and resilient manufacturing systems. The insights provided in this study underscore the importance of continued innovation in integrating these technologies to maintain a competitive edge in the rapidly evolving manufacturing landscape.
This research dealt with the critical integration of advanced modelling techniques and recurrent analysis within network security, with a primary goal of enhancing the critical analysis of network data and improving fault resolution processes. The study focuses on the development of advance, predictive models capable of identifying and mitigating security threats in real-time, leveraging the power of Recurrent Neural Networks (RNNs) alongside other sophisticated machine learning techniques. By harnessing the dynamic capabilities of these models, the research aims to address the growing complexity and sophistication of network threats, which require continuous monitoring and adaptive responses. Also, the study investigates the effectiveness of these advanced models in environments where network conditions are constantly evolving, necessitating security protocols that can dynamically adjust to new and emerging threats. Through rigorous data scrutiny and recurrent analysis, the research seeks to establish fault resolution mechanisms that not only detect and neutralize immediate security breaches but also anticipate potential vulnerabilities before they can be exploited. Ultimately, this research contributes to the advancement of network security by providing a framework that integrates cutting-edge technology with real-time adaptability, ensuring that security measures remain robust and effective in the face of ever-changing digital threats.
This article explores the critical role of deep learning in developing AI-driven cybersecurity solutions, with a particular focus on privacy integrity and information security. It investigates how deep neural networks (DNNs) and advanced machine learning techniques are being used to detect and neutralize cyber threats in real time. The article also considers the implications of these technologies for data privacy, discussing the potential risks and benefits of using AI to protect sensitive information. By examining case studies and current research, the piece provides insights into how organizations can deploy deep learning models to enhance both security and privacy integrity in a digital world.
This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model’s superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.
The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis.
Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.
Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.
Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.
The Internet of Medical Things (IoMT) has become a strategic priority for future e-healthcare because of its ability to improve patient care and its scope of providing more reliable clinical data, increasing efficiency, and reducing costs. It is no wonder that many healthcare institutions nowadays like to harness the benefits offered by the IoMT. In fact, it is an infrastructure with connected medical devices, software applications, and care systems and services. However, the accelerated adoption of connected devices also has a serious side effect: it obscures the broader need to meet the requirements of standard security for modern converged environments (even beyond connected medical devices). Adding up different types and numbers of devices risks creating significant security vulnerabilities. In this paper, we have undertaken a study of various security techniques dedicated to this environment during recent years. This study enables us to classify these techniques and to characterize them in order to benefit from their positive aspects.
In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.
The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers' attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patient's health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices'. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness system's integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy ( 99.31 %) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.
Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.
Internet of things (IoT) is a world wide network and set of paradigms that are intended to allow communications between anything, anytime and anywhere. However, connected objects are in most cases vulnerable due to their constrained resources and the inherent IoT environment conditions, basically, the dynamic aspect, the heterogeneity, and the open and wireless medium of communication. Securing the IoT networks is still an open and challenging issue and the majority of traditional security mechanisms designed so far for Internet doesn’t satisfy IoT security requirements. Recently, the use of emergent technologies such as Artificial Intelligence mechanisms, Blockchain and IoTA as a promising solutions to solve security and privacy problems has shown a yield remarkable performance. In this paper we outline the security requirements proposed for the IoT. We provide a comprehensive taxonomy of the major security issues based on IoT architecture, attack implications and application areas. Furthermore, we tabulate and map the different countermeasures used to solve these threats taking into account new advances in security approaches. Finally, we discuss and compare the enumerated countermeasures for IoT security.
The inherent complexities of Industrial Internet of Things (IIoT) architecture make its security and privacy issues becoming critically challenging. Numerous surveys have been published to review IoT security issues and challenges. The studies gave a general overview of IIoT security threats or a detailed analysis that explicitly focuses on specific technologies. However, recent studies fail to analyze the gap between security requirements of these technologies and their deployed countermeasure in the industry recently. Whether recent industry countermeasure is still adequate to address the security challenges of IIoT environment are questionable. This article presents a comprehensive survey of IIoT security and provides insight into today’s industry countermeasure, current research proposals and ongoing challenges. We classify IIoT technologies into the four-layer security architecture, examine the deployed countermeasure based on CIA+ security requirements, report the deficiencies of today’s countermeasure, and highlight the remaining open issues and challenges. As no single solution can fix the entire IIoT ecosystem, IIoT security architecture with a higher abstraction level using the bottom-up approach is needed. Moving towards a data-centric approach that assures data protection whenever and wherever it goes could potentially solve the challenges of industry deployment.
The Internet of Things (IoT) and the integration of medical devices perform hand-to-hand solutions and comfort to their users. With the inclusion of IoT under medical devices a hybrid (IoMT) is formulated. This features integrated computation and processing of data via dedicated servers. The IoMT is supported with an edge server to assure the mobility of data and information. The backdrop of IoT is a networking framework and hence, the security of such devices under IoT and IoMT is at risk. In this article, a framework and prototype for secure healthcare application processing via blockchain are proposed. The proposed technique uses an optimized Crow search algorithm for intrusion detection and tampering of data extraction in IoT environment. The technique is processed under deep convolution neural networks for comparative analysis and coordination of data security elements. The technique has successfully extracted the instruction detection from un-peer source with a source validation of 100 IoT nodes under initial intervals of 25 nodes based on block access time, block creation, and IPFS storage layer extraction. The proposed technique has a recorded performance efficiency of 92.3%, comparable to trivial intrusion detection techniques under Deep Neural Networks (DNN) supported algorithms.
5G mobile communication systems promote the mobile network to not only interconnect people, but also interconnect and control the machine and other devices. 5G-enabled Internet of Things (IoT) communication environment supports a wide-variety of applications, such as remote surgery, self-driving car, virtual reality, flying IoT drones, security and surveillance and many more. These applications help and assist the routine works of the community. In such communication environment, all the devices and users communicate through the Internet. Therefore, this communication agonizes from different types of security and privacy issues. It is also vulnerable to different types of possible attacks (for example, replay, impersonation, password reckoning, physical device stealing, session key computation, privileged-insider, malware, man-in-the-middle, malicious routing, and so on). It is then very crucial to protect the infrastructure of 5G-enabled IoT communication environment against these attacks. This necessitates the researchers working in this domain to propose various types of security protocols under different types of categories, like key management, user authentication/device authentication, access control/user access control and intrusion detection. In this survey paper, the details of various system models (i.e., network model and threat model) required for 5G-enabled IoT communication environment are provided. The details of security requirements and attacks possible in this communication environment are further added. The different types of security protocols are also provided. The analysis and comparison of the existing security protocols in 5G-enabled IoT communication environment are conducted. Some of the future research challenges and directions in the security of 5G-enabled IoT environment are displayed. The motivation of this work is to bring the details of different types of security protocols in 5G-enabled IoT under one roof so that the future researchers will be benefited with the conducted work.
To identify the key factors and create the landscape of cybersecurity for embedded systems (CSES), an analytical review of the existing research on CSES has been conducted. The common properties of embedded systems, such as mobility, small size, low cost, independence, and limited power consumption when compared to traditional computer systems, have caused many challenges in CSES. The conflict between cybersecurity requirements and the computing capabilities of embedded systems makes it critical to implement sophisticated security countermeasures against cyber-attacks in an embedded system with limited resources, without draining those resources. In this study, twelve factors influencing CSES have been identified: (1) the components; (2) the characteristics; (3) the implementation; (4) the technical domain; (5) the security requirements; (6) the security problems; (7) the connectivity protocols; (8) the attack surfaces; (9) the impact of the cyber-attacks; (10) the security challenges of the ESs; (11) the security solutions; and (12) the players (manufacturers, legislators, operators, and users). A Multiple Layers Feedback Framework of Embedded System Cybersecurity (MuLFESC) with nine layers of protection is proposed, with new metrics of risk assessment. This will enable cybersecurity practitioners to conduct an assessment of their systems with regard to twelve identified cybersecurity aspects. In MuLFESC, the feedback from the systemcomponents layer to the system-operations layer could help implement “Security by Design” in the design stage at the bottom layer. The study provides a clear landscape of CSES and, therefore, could help to find better comprehensive solutions for CSES.
With IoT devices becoming more ingrained into everyday life and business, attacks on Internet-of-Things (IoT) systems can be costly and, in extreme cases, cause life-threatening situations and huge economic loss. Denial-of-Service (DoS) attacks have been well studied in cybersecurity, partly because of the Mirai malware which has shown extensive damage to an insecure network. However, Man-in-the-Middle (MITM) attacks have been largely overlooked especially in IoT networks. In this paper, we introduce a new scheme of a Man-in-the-Middle (MITM) attack on IoT devices that utilize the Message Queuing Telemetry Transport (MQTT) protocol for communications. This attack scheme consists of an MQTT Parser that is created to dissect and alter MQTT messages at the bit level, and a novel BERT-based adversarial model that generates malicious messages using an approach inspired by GAN. We present the design of this attack in order to show how a sophisticated attack could lead to serious damage that is difficult for typical security defense mechanisms to detect. We set up a test-bed using IoT hardware and software including Raspberry Pi, WiFi Pineapple, Mosquitto, etc. to conduct experiments. We show that our designed attack scheme successfully evades logistic regression, random forest, K-nearest neighbor, and support vector machine (SVM) based anomaly detection models. Multi-Layer Perceptron fares better against our model, but such use of deep neural networks on typical IoT devices is rather restricted due to the computation cost. In summary, the results show that the MITM attack is effective against a wide range of typical anomaly detection mechanisms. KEYWORDS Internet of things (IoT) security, man-in-the-middle attack, denial-of-service attack, anomaly detection, MQTT, BERT.
Over the last few decades, sustainable computing has been widely used in areas like social computing, artificial intelligence-based agent systems, mobile computing, and Internet of Things (IoT). There are social, economic, and commercial impacts of IoT on human lives. However, IoT nodes are generally power-constrained with data transmission using an open channel, i.e., Internet which opens the gates for various types of attacks on them. In this context, several efforts are initiated to deal with the evolving security issues in IoT systems and make them self-sufficient to harvest energy for smooth functioning. Motivated by these facts, in this paper, we explore the evolving vulnerabilities in IoT devices. We provide a state-of-the-art survey that addresses multiple dimensions of the IoT realm. Moreover, we provide a general overview of IoT, Sustainable IoT, its architecture, and the Internet Engineering Task Force (IETF) protocol suite. Subsequently, we explore the open-source tools and datasets for the proliferation in research and growth of IoT. A detailed taxonomy of attacks associated with various vulnerabilities is also presented in the text. Then we have specifically focused on the IoT Vulnerability Assessment techniques followed by a case study on sustainability of Smart Agriculture. Finally, this paper outlines the emerging challenges related to IoT and its sustainability, and opening the doors for the beginners to start research in this promising area.
The Internet of Things (IoT) has experienced constant growth in the number of devices deployed and the range of applications in which such devices are used. They vary widely in size, computational power, capacity storage, and energy. The explosive growth and integration of IoT in different domains and areas of our daily lives has created an Internet of Vulnerabilities (IoV). In the rush to build and implement IoT devices, security and privacy have not been adequately addressed. IoT devices, many of which are highly constrained, are vulnerable to cyber attacks, which threaten the security and privacy of users and systems. This survey provides a comprehensive overview of IoT in regard to areas of application, security architecture frameworks, recent security and privacy issues in IoT, as well as a review of recent similar studies on IoT security and privacy. In addition, the paper presents a comprehensive taxonomy of attacks on IoT based on the three-layer architecture model; perception, network, and application layers, as well as a suggestion of the impact of these attacks on CIA objectives in representative devices, are presented. Moreover, the study proposes mitigations and countermeasures, taking a multi-faceted approach rather than a per layer approach. Open research areas are also covered to provide researchers with the most recent research urgent questions in regard to securing IoT ecosystem.
In the recent era, the security issues affecting the future Internet-of-Things (IoT) standards has fascinated noteworthy consideration from numerous research communities. In this view, numerous assessments in the form of surveys were proposed highlighting several future IoT-centric subjects together with threat modeling, Intrusion Detection Systems (IDS), and various emergent technologies. In contrast, in this article, we have focused exclusively on the emerging IoT-related vulnerabilities. This article is a multi-fold survey that emphasizes on understanding the crucial causes of novel vulnerabilities in IoT paradigms and issues in existing research. Initially, we have emphasized on different layers of IoT architecture and highlight various emerging security challenges associated with each layer along with the key issues of different IoT systems. Secondly, we discuss the exploitation, detection and defense methodologies of IoT malware-enabled Distributed Denial of Service (DDoS), sybil, and collusion attack capabilities. We have also discussed numerous state-of-the-art strategies for intrusion detection and methods for IDS setup in future IoT systems. Third, we have presented a brief classification of existing IoT authentication protocols and a comparative analysis of such protocols based on different IoT-enabled cyber-attacks. For conducting a real-time future IoT research, we have presented some emerging blockchain solutions. We have also discussed a comparative examination of some of the recently developed simulation tools and IoT test beds that are characterized based on different layers of IoT infrastructure. We have also outlined some of the open issues and future research directions and also facilitates the readers with broad classification of existing surveys in this domain that addresses several scopes related to the IoT paradigm. This survey article focusses in enabling IoT-related research activities by comparing and merging scattered surveys in this domain.
As we all know that the technology is projected to be next to humans very soon because of its holistic growth. Now-a-days, we see a lot of applications that are making our lives comfortable such as smart cars, smart homes, smart traffic management, smart offices, smart medical consultation, smart cities, etc. All such facilities are in the reach of a common man because of the advancement in Information and Communications Technology (ICT). Because of this advancement, new computing and communication environment such as Internet of Things (IoT) came into picture. Lot of research work is in progress in IoT domain which helps for the overall development of the society and makes the lives easy and comfortable. But in the resource constrained environment of Wireless Sensor Network (WSN) and IoT, it is almost inconceivable to establish a fully secure system. As we are moving forward very fast, technology is becoming more and more vulnerable to the security threats. In future, the number of Internet connected people will be less than the smart objects so we need to prepare a robust system for keeping the above mentioned environments safe and standardized it for the smooth conduction of communication among IoT objects. In this survey paper, we provide the details of threat model applicable for the security of WSN and IoT based communications. We also discuss the security requirements and various attacks possible in WSN and IoT based communication environments. The emerging projects of WSNs integrated to IoT are also briefed. We then provide the details of different architectures of WSN and IoT based communication environments. Next, we discuss the current issues and challenges related to WSN and IoT. We also provide a critical literature survey of recent intrusion detection protocols for IoT and WSN environments along with their comparative analysis. A taxonomy of security and privacy-preservation protocols in WSN and IoT is also highlighted. Finally, we discuss some research challenges which need to be addressed in the coming future.
The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, privileged-insider, remote hijacking, password guessing and denial of service (DoS) attacks, and malware attacks. Among these attacks, the attacks which are performed through the malware botnet (i.e., Mirai) are the malignant attacks. The existence of malware botnets leads to attacks on confidentiality, integrity, authenticity and availability of the data and other resources of the system. In presence of such attacks, the sensitive data of IoT communication may be disclosed, altered or even may not be available to the authorized users. Therefore, it becomes essential to protect the IoT/IoMT environment from malware attacks. In this review paper, we first perform the study of various types of malware attacks, and their symptoms. We also discuss some architectures of IoT environment along with their applications. Next, a taxonomy of security protocols in IoT environment is provided. Moreover, we conduct a comparative study on various existing schemes for malware detection and prevention in IoT environment. Finally, some future research challenges and directions of malware detection in IoT/IoMT environment are highlighted.
As Internet of Things (IoT) involvement increases in our daily lives, several security and privacy concerns like linkability, unauthorized conversations, and side-channel attacks are raised. If they are left untouched, such issues may threaten the existence of IoT. They derive from two main reasons. One is that IoT objects are equipped with limited capabilities in terms of computation power, memory, and bandwidth which hamper the direct implementation of traditional Internet security techniques. The other reason is the absence of widely-accepted IoT security and privacy guidelines and their appropriate implementation techniques. Such guidelines and techniques would greatly assist IoT stakeholders like developers and manufacturers, paving the road for building secure IoT systems from the start and, thus, reinforcing IoT security and privacy by design. In order to contribute to such objective, we first briefly discuss the primary IoT security goals and recognize IoT stakeholders. Second, we propose a comprehensive list of IoT security and privacy guidelines for the edge nodes and communication levels of IoT reference architecture. Furthermore, we point out the IoT stakeholders such as customers and manufacturers who will benefit most from these guidelines. Moreover, we identify a set of implementation techniques by which such guidelines can be accomplished, and possible attacks against previously-mentioned levels can be alleviated. Third, we discuss the challenges of IoT security and privacy guidelines, and we briefly discuss digital rights management in IoT. Finally, through this survey, we suggest several open issues that require further investigation in the future. To the best of the authors’ knowledge, this work is the first survey that covers the above-mentioned objectives.
Nowadays, the industrial sector is being challenged by several cybersecurity concerns. Direct attacks by malicious persons and (or) software form part of the severe jeopardies of industrial control systems (ICSs). These affect products/production qualities, brand reputations, sales revenues, and aggravate the risks to health and safety of human lives. These have been enabled due to progressive adoption of technology trends like Industry 4.0, BYOD, mobile computing, and Internet-of-Things (IoT), in the quest for improved relevance and value of production decisions, minimised operational overheads, optimum resource utilisation, markets globalisation, etc. However, several security vulnerabilities and risks have also emerged, and are increasingly being exploited in the industrial sector especially manufacturing. To manage this phenomenon, refined and holistic (combining people, process, and technology perspectives) security strategies and solutions are required to enhance security in ICS. This paper offers an insightful review of possible solution path beginning with the understanding of ICS security trends relative to cyber threats, vulnerabilities, attacks and patterns, agents, risks, and the impacts of all these on the industrial environment and entities that depend on it. Such episteme can improve security awareness, proficiency for respective stakeholders, and advance the development of appropriate security mechanisms, and adoption of recommendations.
The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs) and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet, communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and Cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on Fog with Fuzzy Logic decision making and is achieved on Cloud with Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed.
Internet of Things (IoT) is a network of several hardware and software systems which is broadly based upon internet services and several state‐of‐the‐art sensing and communication technologies. The emergence of 5G technology will witness a further surge in the growth of IoT across the world but simultaneously security concerns pertinent to the IoT technology also need rigorous evaluations. This article will present a thorough survey of the security challenges in an IoT network, recent cases of attacks on IoT technology, communication protocols prevalent in IoT systems and the role of artificial intelligence (AI) in IoT security. For the first time, all the major attributes related to IoT security along with potential solutions using AI are reviewed and articulated together. This work would act as a useful resource for understanding useful perspectives in future research focused around the development of more secured IoT communication protocols as well as AI tools for handling privacy and security in IoT.
The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT application and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.
Nowadays, data is created by humans as well as automatically collected by physical things, which embed electronics, software, sensors and network connectivity. Together, these entities constitute the Internet of Things (IoT). The automated analysis of its data can provide insights into previously unknown relationships between things, their environment and their users, facilitating an optimization of their behavior. Especially the real-time analysis of data, embedded into physical systems, can enable new forms of autonomous control. These in turn may lead to more sustainable applications, reducing waste and saving resources
IoT's distributed and dynamic nature, resource constraints of sensors and embedded devices as well as the amounts of generated data are challenging even the most advanced automated data analysis methods known today. In particular, the IoT requires a new generation of distributed analysis methods.
Many existing surveys have strongly focused on the centralization of data in the cloud and big data analysis, which follows the paradigm of parallel high-performance computing. However, bandwidth and energy can be too limited for the transmission of raw data, or it is prohibited due to privacy constraints. Such communication-constrained scenarios require decentralized analysis algorithms which at least partly work directly on the generating devices.
After listing data-driven IoT applications, in contrast to existing surveys, we highlight the differences between cloudbased and decentralized analysis from an algorithmic perspective. We present the opportunities and challenges of research on communication-efficient decentralized analysis algorithms. Here, the focus is on the difficult scenario of vertically partitioned data, which covers common IoT use cases. The comprehensive bibliography aims at providing readers with a good starting point for their own work
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