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1
Chen, Chien-Ying, Monowar Hasan, and Sibin Mohan. "Securing Real-Time Internet-of-Things." Sensors 18, no. 12 (December 10, 2018): 4356. http://dx.doi.org/10.3390/s18124356.
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Modern embedded and cyber-physical systems are ubiquitous. Many critical cyber-physical systems have real-time requirements (e.g., avionics, automobiles, power grids, manufacturing systems, industrial control systems, etc.). Recent developments and new functionality require real-time embedded devices to be connected to the Internet. This gives rise to the real-time Internet-of-things (RT-IoT) that promises a better user experience through stronger connectivity and efficient use of next-generation embedded devices. However, RT-IoT are also increasingly becoming targets for cyber-attacks, which is exacerbated by this increased connectivity. This paper gives an introduction to RT-IoT systems, an outlook of current approaches and possible research challenges towards secure RT-IoT frameworks.
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Dhirani, Lubna Luxmi, Eddie Armstrong, and Thomas Newe. "Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap." Sensors 21, no. 11 (June 5, 2021): 3901. http://dx.doi.org/10.3390/s21113901.
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Industrial IoT (IIoT) is a novel concept of a fully connected, transparent, automated, and intelligent factory setup improving manufacturing processes and efficiency. To achieve this, existing hierarchical models must transition to a fully connected vertical model. Since IIoT is a novel approach, the environment is susceptible to cyber threat vectors, standardization, and interoperability issues, bridging the gaps at the IT/OT ICS (industrial control systems) level. IIoT M2M communication relies on new communication models (5G, TSN ethernet, self-driving networks, etc.) and technologies which require challenging approaches to achieve the desired levels of data security. Currently there are no methods to assess the vulnerabilities/risk impact which may be exploited by malicious actors through system gaps left due to improper implementation of security standards. The authors are currently working on an Industry 4.0 cybersecurity project and the insights provided in this paper are derived from the project. This research enables an understanding of converged/hybrid cybersecurity standards, reviews the best practices, and provides a roadmap for identifying, aligning, mapping, converging, and implementing the right cybersecurity standards and strategies for securing M2M communications in the IIoT.
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Mahmood, Mohammed, and Jassim Abdul-Jabbar. "Securing Industrial Internet of Things (Industrial IoT)- A Reviewof Challenges and Solutions." Al-Rafidain Engineering Journal (AREJ) 28, no. 1 (March 1, 2023): 312–20. http://dx.doi.org/10.33899/rengj.2022.135292.1196.
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George, Gemini, and Sabu M. Thampi. "A Graph-Based Security Framework for Securing Industrial IoT Networks From Vulnerability Exploitations." IEEE Access 6 (2018): 43586–601. http://dx.doi.org/10.1109/access.2018.2863244.
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Dakhnovich, A. D., D. A. Moskvin, and D. P. Zegzhda. "Approach for Securing Network Communications Modelling Based on Smart Multipath Routing." Nonlinear Phenomena in Complex Systems 23, no. 4 (December 4, 2020): 386–96. http://dx.doi.org/10.33581/1561-4085-2020-23-4-386-396.
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Digital transformation, or Industry 4.0, is already changing manufacturing processes as it brings more automation to standardized Industrial Control Systems (ICS) based systems such as Supervisory Control and Data Acquisition (SCADA) systems. It is performed by the means of cyber-physical systems such as Internet of Things (IoT). For now, these “Things” are communicating in a new network area, where peer-to-peer communications are widely used. Such networks are responsible for real life processes safety. However, such shift also extends a threat vectors and entry points for an adversary inside the industrial segments. In the paper, new cybersecurity challenges on the Industrial Internet of Things network segments are considered as well as known practices to mitigate some of them. As a result, a peer-to-peer smart multipath network routing based on garlic routing is proposed to model secure network communications in IoT field. An approach is aimed to be used on the IoT field to tackle all of the network-scoped cybersecurity challenges.
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Yas, Harith, and Manal M. Nasir. "Securing the IoT: An Efficient Intrusion Detection System Using Convolutional Network." Journal of Cybersecurity and Information Management 1, no. 1 (2020): 30–37. http://dx.doi.org/10.54216/jcim.010105.
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The Internet of Things (IoT) is an ever-expanding network of interconnected devices that enables various applications, such as smart homes, smart cities, and industrial automation. However, with the proliferation of IoT devices, security risks have increased significantly, making it necessary to develop effective intrusion detection systems (IDS) for IoT networks. In this paper, we propose an efficient IDS for complex IoT environments based on convolutional neural networks (CNNs). Our approach uses IoT traffics as input to our CNN architecture to capture representational knowledge required to discriminate different forms of attacks. Our system achieves high accuracy and low false positive rates, even in the presence of complex and dynamic network traffic patterns. We evaluate the performance of our system using public datasets and compare it with other cutting-edge IDS approaches. Our results show that the proposed system outperforms the other approaches in terms of accuracy and false positive rates. The proposed IDS can enhance the security of IoT networks and protect them against various types of cyber-attacks.
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Kurdi, Hassan, and Vijey Thayananthan. "A Multi-Tier MQTT Architecture with Multiple Brokers Based on Fog Computing for Securing Industrial IoT." Applied Sciences 12, no. 14 (July 16, 2022): 7173. http://dx.doi.org/10.3390/app12147173.
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With the rapid growth of internet-connected devices and their resource-constrained capabilities, the current authentication mechanisms are unable to meet the complex IoT application requirements, such as in the Industrial Internet of Things (IIoT), due to the increased computation, communication, and storage overhead arising from these mechanisms. In the IIoT, machine-to-machine (M2M) communication is an underlying technology where devices (e.g., sensors, actuators, and controllers) can be enabled to exchange information autonomously; thus, the massive data generated by these devices can increase latency, network congestion, and the complexity of security management. Message queue telemetry transport (MQTT) is one of the promising M2M protocols used in the IoT that could encounter such issues because it relies on a central broker in the cloud and implements a heavyweight authentication mechanism based on TLS. Therefore, this paper proposes an MQTT architecture with multi-tier brokers based on fog computing, where each broker is deployed with an authentication manager. In addition, the paper presents a lightweight mutual authentication scheme based on hash function and XOR operation. Comparing the results given in the benchmark, the overall performance of our scheme shows that storage and communication overheads are reduced to 89% and 23%, respectively. Furthermore, our system can resist against several cyberattacks and provide scalability.
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Elkanishy, Abdelrahman, Paul M. Furth, Derrick T. Rivera, and Ahameed A. Badawy. "Low-overhead Hardware Supervision for Securing an IoT Bluetooth-enabled Device: Monitoring Radio Frequency and Supply Voltage." ACM Journal on Emerging Technologies in Computing Systems 18, no. 1 (January 31, 2022): 1–28. http://dx.doi.org/10.1145/3468064.
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Over the past decade, the number of Internet of Things (IoT) devices increased tremendously. In particular, the Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT) expanded dramatically. Resource restrictions on IoT devices and the insufficiency of software security solutions raise the need for smart Hardware-Assisted Security (HAS) solutions. These solutions target one or more of the three C’s of IoT devices: Communication, Control, and Computation. Communication is an essential technology in the development of IoT. Bluetooth is a widely-used wireless communication protocol in small portable devices due to its low energy consumption and high transfer rates. In this work, we propose a supervisory framework to monitor and verify the operation of a Bluetooth system-on-chip (SoC) in real-time. To verify the operation of the Bluetooth SoC, we classify its transmission state in real-time to ensure a secure connection. Our overall classification accuracy is measured as 98.7%. We study both power supply current (IVDD) and RF domains to maximize the classification performance and minimize the overhead of our proposed supervisory system.
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Kristen, Erwin, Reinhard Kloibhofer, Vicente Hernández Díaz, and Pedro Castillejo. "Security Assessment of Agriculture IoT (AIoT) Applications." Applied Sciences 11, no. 13 (June 23, 2021): 5841. http://dx.doi.org/10.3390/app11135841.
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Cybersecurity is an important field in our digital world. It protects computer systems and communication networks against theft or sabotage of information to guarantee trouble-free operation in a trustworthy working environment. This article gives an overview of a cybersecurity assessment process and an appropriate Cybersecurity Management (CSM) implementation for future digital agriculture applications. The cybersecurity assessment follows the IEC 62443 cybersecurity standard for Industrial Automation Control Systems (IACS), adapted to Agriculture Automation Control Systems (AACS). However, the research results showed application differences; thus, an expansion of the standard is necessary to fill the existing open security gaps in agriculture. Agriculture differs from industrial control systems because of the outdoor located field area, which requires other forms of security. An appropriate cybersecurity standard for the agriculture domain is not currently available. However, such a standard will be necessary to define generally applicable procedures to protect agricultural assets against cyberattacks. The cybersecurity standards and regulations existing today (2021) are not sufficient for securing the agriculture domain against new and domain-specific cyberattacks. This article describes some of the cyber vulnerabilities identified and provides initial recommendations for addressing them.
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Juma, Mazen, Fuad AlAttar, and Basim Touqan. "Securing Big Data Integrity for Industrial IoT in Smart Manufacturing Based on the Trusted Consortium Blockchain (TCB)." IoT 4, no. 1 (February 6, 2023): 27–55. http://dx.doi.org/10.3390/iot4010002.
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The smart manufacturing ecosystem enhances the end-to-end efficiency of the mine-to-market lifecycle to create the value chain using the big data generated rapidly by edge computing devices, third-party technologies, and various stakeholders connected via the industrial Internet of things. In this context, smart manufacturing faces two serious challenges to its industrial IoT big data integrity: real-time transaction monitoring and peer validation due to the volume and velocity dimensions of big data in industrial IoT infrastructures. Modern blockchain technologies as an embedded layer substantially address these challenges to empower the capabilities of the IIoT layer to meet the integrity requirements of the big data layer. This paper presents the trusted consortium blockchain (TCB) framework to provide an optimal solution for big data integrity through a secure and verifiable hyperledger fabric modular (HFM). The TCB leverages trustworthiness in heterogeneous IIoT networks of governing end-point peers to achieve strong integrity for big data and support high transaction throughput and low latency of HFM contents. Our proposed framework drives the fault-tolerant properties and consensus protocols to monitor malicious activities of tunable peers if compromised and validates the signed evidence of big data recorded in real-time HFM operated over different smart manufacturing environments. Experimentally, the TCB has been evaluated and reached tradeoff results of throughput and latency better than the comparative consortium blockchain frameworks.
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Hussain, Faisal, Syed Ghazanfar Abbas, Ghalib A. Shah, Ivan Miguel Pires, Ubaid U. Fayyaz, Farrukh Shahzad, Nuno M. Garcia, and Eftim Zdravevski. "A Framework for Malicious Traffic Detection in IoT Healthcare Environment." Sensors 21, no. 9 (April 26, 2021): 3025. http://dx.doi.org/10.3390/s21093025.
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The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.
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Plaga, Sven, Norbert Wiedermann, Simon Duque Anton, Stefan Tatschner, Hans Schotten, and Thomas Newe. "Securing future decentralised industrial IoT infrastructures: Challenges and free open source solutions." Future Generation Computer Systems 93 (April 2019): 596–608. http://dx.doi.org/10.1016/j.future.2018.11.008.
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Zahid, Huma, Sadaf Hina, Muhammad Faisal Hayat, and Ghalib A. Shah. "Agentless Approach for Security Information and Event Management in Industrial IoT." Electronics 12, no. 8 (April 12, 2023): 1831. http://dx.doi.org/10.3390/electronics12081831.
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The Internet of Things (IoT) provides ease of real-time communication in homes, industries, health care, and many other dependable and interconnected sectors. However, in recent years, smart infrastructure, including cyber-physical industries, has witnessed a severe disruption of operation due to privilege escalation, exploitation of misconfigurations, firmware hijacking, malicious node injection, botnets, and other malware infiltrations. The proposed agentless module for Wazuh security information and event management (SIEM) solution contributes to securing small- to large-scale IoT networks of industry 4.0. An agentless module is implemented by vigilantly examining the IoT device traffic without installing any agent or software on the endpoints. In the proposed research scheme, a module sniffs the network traffic of IoT devices captured from the gateway and passes it to a machine learning model for initial detection and prediction. The output of the ML model is embedded in the JSON log format and passed through the Wazuh agent to the Wazuh server where a decoder is added that decodes the network traffic logs. For event monitoring in Wazuh, industrial protocols are also thoroughly analyzed, and the feature set is determined. These features are used to write rules which are tested on the SWaT dataset, utilizing a common industrial protocol (CIP) for communication. Custom and dynamic rules are written at the Wazuh end to generate alerts to respond to any anomaly detected by the machine learning (ML) model or in the protocols used. Finally, in case of any event or an attack is detected, the alerts are fired on the Wazuh dashboard. This agentless SIEM solution has practical implications for the security of the industrial control systems of industry 4.0.
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Yankson, Benjamin, Tyler Loucks, Andrea Sampson, and Chelsea Lojano. "Robots Security Assessment and Analysis Using Open-Source Tools." International Conference on Cyber Warfare and Security 18, no. 1 (February 28, 2023): 449–56. http://dx.doi.org/10.34190/iccws.18.1.1019.
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The Internet of things (IoT) has revolutionized many aspects of the world, including industrial systems, automobiles, home automation, and surveillance, to name a few. IoT has offered a multitude of conveniences for our daily lives, such as being able to control our thermostats remotely, view our home surveillance cameras while away, or have a smart television that can surf the web. However, the widespread adoption of IoT devices combined with their vast device vulnerabilities results in significant security risks reinforcing the need for more robust default security controls and public awareness. As such, this paper aims to discover and document security vulnerabilities in the Asus Zenbo Junior IoT robot, along with providing a few best practices when securing smart home devices. This work presents an experiment using several security vulnerability assessment tools such as Nmap and OpenVAS scans to assess cybersecurity vulnerability currently present on Zenbo based on the 4P forensic investigative framework. The result of the experiment shows multiple open ports were discovered, along with miscellaneous information that an attacker could use to their advantage to attack the Zenbo robot. Based on the result, this work presents various security precautions that can help users protect against cyber-attack.
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Kolhar, Manjur, and Sultan Mesfer Aldossary. "A Deep Learning Approach for Securing IoT Infrastructure with Emphasis on Smart Vertical Networks." Designs 7, no. 6 (December 1, 2023): 139. http://dx.doi.org/10.3390/designs7060139.
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As a result of the Internet of Things (IoT), smart city infrastructure has been able to advance, enhancing efficiency and enabling remote management. Despite this, this interconnectivity poses significant security and privacy concerns, as cyberthreats are rapidly adapting to exploit IoT vulnerabilities. In order to safeguard privacy and ensure secure IoT operations, robust security strategies are necessary. To detect anomalies effectively, intrusion detection systems (IDSs) must employ sophisticated algorithms capable of handling complex and voluminous datasets. A novel approach to IoT security is presented in this paper, which focuses on safeguarding smart vertical networks (SVNs) integral to sector-specific IoT implementations. It is proposed that a deep learning-based method employing a stacking deep ensemble model be used, selected for its superior performance in managing large datasets and its ability to learn intricate patterns indicative of cyberattacks. Experimental results indicate that the model is exceptionally accurate in identifying cyberthreats, exceeding other models, with a 99.8% detection rate for the ToN-IoT dataset and 99.6% for the InSDN dataset. The paper aims not only to introduce a robust algorithm for IoT security, but also to demonstrate its efficacy through comprehensive testing. We selected a deep learning ensemble model due to its proven track record in similar applications and its ability to maintain the integrity of IoT systems in smart cities.
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Hasan, Ahmad, Muazzam A. Khan, Balawal Shabir, Arslan Munir, Asad Waqar Malik, Zahid Anwar, and Jawad Ahmad. "Forensic Analysis of Blackhole Attack in Wireless Sensor Networks/Internet of Things." Applied Sciences 12, no. 22 (November 11, 2022): 11442. http://dx.doi.org/10.3390/app122211442.
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The internet of things (IoT) is prone to various types of denial of service (DoS) attacks due to their resource-constrained nature. Extensive research efforts have been dedicated to securing these systems, but various vulnerabilities remain. Notably, it is challenging to maintain the confidentiality, integrity, and availability of mobile ad hoc networks due to limited connectivity and dynamic topology. As critical infrastructure including smart grids, industrial control, and intelligent transportation systems is reliant on WSNs and IoT, research efforts that forensically investigate and analyze the cybercrimes in IoT and WSNs are imperative. When a security failure occurs, the causes, vulnerabilities, and facts behind the failure need to be revealed and examined to improve the security of these systems. This research forensically investigates the performance of the ad hoc IoT networks using the ad hoc on-demand distance vector (AODV) routing protocol under the blackhole attack, which is a type of denial of service attack detrimental to IoT networks. This work also examines the traffic patterns in the network and nodes to assess the attack damage and conducts vulnerability analysis of the protocol to carry out digital forensic (DF) investigations. It further reconstructs the networks under different modes and parameters to verify the analysis and provide suggestions to design roubust routing protocols.
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Kelli, Vasiliki, Vasileios Argyriou, Thomas Lagkas, George Fragulis, Elisavet Grigoriou, and Panagiotis Sarigiannidis. "IDS for Industrial Applications: A Federated Learning Approach with Active Personalization." Sensors 21, no. 20 (October 11, 2021): 6743. http://dx.doi.org/10.3390/s21206743.
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Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.
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Pal, Shantanu, and Zahra Jadidi. "Analysis of Security Issues and Countermeasures for the Industrial Internet of Things." Applied Sciences 11, no. 20 (October 10, 2021): 9393. http://dx.doi.org/10.3390/app11209393.
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Industrial Internet of Things (IIoT) can be seen as an extension of the Internet of Things (IoT) services and applications to industry with the inclusion of Industry 4.0 that provides automation, reliability, and control in production and manufacturing. IIoT has tremendous potential to accelerate industry automation in many areas, including transportation, manufacturing, automobile, marketing, to name a few places. When the benefits of IIoT are visible, the development of large-scale IIoT systems faces various security challenges resulting in many large-scale cyber-attacks, including fraudulent transactions or damage to critical infrastructure. Moreover, a large number of connected devices over the Internet and resource limitations of the devices (e.g., battery, memory, and processing capability) further pose challenges to the system. The IIoT inherits the insecurities of the traditional communication and networking technologies; however, the IIoT requires further effort to customize the available security solutions with more focus on critical industrial control systems. Several proposals discuss the issue of security, privacy, and trust in IIoT systems, but comprehensive literature considering the several aspects (e.g., users, devices, applications, cascading services, or the emergence of resources) of an IIoT system is missing in the present state of the art IIoT research. In other words, the need for considering a vision for securing an IIoT system with broader security analysis and its potential countermeasures is missing in recent times. To address this issue, in this paper, we provide a comparative analysis of the available security issues present in an IIoT system. We identify a list of security issues comprising logical, technological, and architectural points of view and consider the different IIoT security requirements. We also discuss the available IIoT architectures to examine these security concerns in a systematic way. We show how the functioning of different layers of an IIoT architecture is affected by various security issues and report a list of potential countermeasures against them. This study also presents a list of future research directions towards the development of a large-scale, secure, and trustworthy IIoT system. The study helps understand the various security issues by indicating various threats and attacks present in an IIoT system.
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Burange, Anup W., and Vaishali M. Deshmukh. "Securing IoT Attacks: A Machine Learning Approach for Developing Lightweight Trust-Based Intrusion Detection System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (September 1, 2023): 14–22. http://dx.doi.org/10.17762/ijritcc.v11i7.7788.
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The routing process in the Internet of Things (IoT) presents challenges in industrial applications due to its complexity, involving multiple devices, critical decision-making, and accurate data transmission. The complexity further increases with dynamic IoT devices, which creates opportunities for potential intruders to disrupt routing. Traditional security measures are inadequate for IoT devices with limited battery capabilities. Although RPL (Routing Protocol for Low Energy and Lossy Networks) is commonly used for IoT routing, it remains vulnerable to security threats. This study aims to detect and isolate three routing attacks on RPL: Rank, Sybil, and Wormhole. To achieve this, a lightweight trust-based secured routing system is proposed, utilizing machine learning techniques to derive values for devices in new networks, where initial trust values are unavailable. The system demonstrates successful detection and isolation of attacks, achieving an accuracy of 98.59%, precision of 98%, recall of 99%, and f-score of 98%, thereby reinforcing its effectiveness. Attacker nodes are identified and promptly disabled, ensuring a secure routing environment. Validation on a generated dataset further confirms the reliability of the system.
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Sharma, Parjanay, Siddhant Jain, Shashank Gupta, and Vinay Chamola. "Role of machine learning and deep learning in securing 5G-driven industrial IoT applications." Ad Hoc Networks 123 (December 2021): 102685. http://dx.doi.org/10.1016/j.adhoc.2021.102685.
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Elsisi, Mahmoud, Karar Mahmoud, Matti Lehtonen, and Mohamed M. F. Darwish. "Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters." Sensors 21, no. 2 (January 12, 2021): 487. http://dx.doi.org/10.3390/s21020487.
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The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.
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Mudassir, Mohammed, Devrim Unal, Mohammad Hammoudeh, and Farag Azzedin. "Detection of Botnet Attacks against Industrial IoT Systems by Multilayer Deep Learning Approaches." Wireless Communications and Mobile Computing 2022 (May 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/2845446.
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Industry 4.0 is the next revolution in manufacturing technology that is going to change the production and distribution of goods and services within the following decade. Powered by different enabling technologies that are also being developed simultaneously, it has the potential to create radical changes in our societies such as by giving rise to highly-integrated smart cities. The Industrial Internet of Things (IIoT) is one of the main areas of development for Industry 4.0. These IIoT devices are used in mission-critical sectors such as the manufacturing industry, power generation, and healthcare management. However, smart factories and cities can only function when threats to cyber security, data privacy, and information integrity are properly managed. In this regard, securing IIoT devices and their networks is vital to preserving data and privacy. The use of artificial intelligence is an enabler for more secure IIoT systems. In this study, we propose high-performing deep learning models for the classification of botnet attacks that commonly affect IIoT devices and networks. Evaluation of results shows that deep learning models such as the artificial neural network (ANN), the long short-term memory (LSTM), and the gated recurrent unit (GRU) can successfully be used for classifications of IIoT malware attacks with an accuracy of up to 99 % .
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Alharbi, Muhammad H., and Omar H. Alhazmi. "User Authentication Scheme for Internet of Things Using Near-Field Communication." International Journal of Reliability, Quality and Safety Engineering 27, no. 05 (March 23, 2020): 2040012. http://dx.doi.org/10.1142/s0218539320400124.
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In the Internet of things (IoT), the user authentication process is becoming more critical with the growing number of the services provided by IoT. Securing access to these services by the user authentication process leads to high security to prevent any attack on the IoT network. The approach keeps the private information secured efficiently and ensuring that only authorized users can access this information. The proposed scheme uses only Chaskey hash function and XOR operation. The security analysis proves that it is immune to different types of attacks. Furthermore, the scheme is faster, lighter on resources and energy consumption compared to other existing schemes. Finally, we compare the proposed scheme to several other existing schemes on several aspects highlighting main differences.
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Idrees, Sheikh Mohammad, Mariusz Nowostawski, Roshan Jameel, and Ashish Kumar Mourya. "Security Aspects of Blockchain Technology Intended for Industrial Applications." Electronics 10, no. 8 (April 16, 2021): 951. http://dx.doi.org/10.3390/electronics10080951.
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Blockchain technology plays a significant role in the industrial development. Many industries can potentially benefit from the innovations blockchain decentralization technology and privacy protocols offer with regard to securing, data access, auditing and managing transactions within digital platforms. Blockchain is based on distributed and secure decentralized protocols in which there is no single authority, and no single point of control; the data blocks are generated, added, and validated by the nodes of the network themselves. This article provides insights into the current developments within blockchain technology and explores its ability to revolutionize the multiple industrial application areas such as supply chain industry, Internet of Things (IoT), healthcare, governance, finance and manufacturing. It investigates and provides insights into the security issues and threats related to the blockchain implementations by assessing the research through a systematic literature review. This article proposes possible solutions in detail for enhancing the security of the blockchain for industrial applications along with significant directions for future explorations. The study further suggests how in recent years the adoption of blockchain technology by multiple industrial sectors has gained momentum while in the finance sector it is touching new heights day by day.
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Chaudhary, Gopal, Smriti Srivastava, and Manju Khari. "Generative Edge Intelligence for Securing IoT-assisted Smart Grid against Cyber-Threats." International Journal of Wireless and Ad Hoc Communication 5, no. 2 (2022): 19–34. http://dx.doi.org/10.54216/ijwac.050202.
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The critical dependence of industrial smart grid systems on cutting-edge Internet of Things (IoT) technologies has made these systems more susceptible to a diverse array of assaults. This consequently puts at risk the integrity of energy data as well as the safety of energy management activities that depend on those data. This study offers an adversarial federated learning framework for threat detection in a cloud-assisted smart grid system. We refer to this framework as FSEI-Net. The goal of this study is to reduce the risk posed by this threat. A unique semi-supervised edge intelligence network (SEI-Net) is presented in the FSEI-Net to enable semi-supervised training using labeled and unlabeled data in the edge tier. This was accomplished by the FSEI-Net. We present federated training to enable remote edge servers to work together on training a semi-supervised detector without disclosing their own private local data. This is accomplished through cooperative training. To facilitate communication between cloud and edge layers that is both secure and respectful of users' privacy, a reputation-based block chain is introduced in the FSEI-Net. The outcomes from the practical applications demonstrate that the effectiveness of the proposed FSEI-Net over the most recent cutting-edge detection approaches is validated.
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Chaudhary, Gopal, Smriti Srivastava, and Manju Khari. "Generative Edge Intelligence for Securing IoT-assisted Smart Grid against Cyber-Threats." International Journal of Wireless and Ad Hoc Communication 6, no. 1 (2023): 38–49. http://dx.doi.org/10.54216/ijwac.060104.
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The critical dependence of industrial smart grid systems on cutting-edge Internet of Things (IoT) technologies has made these systems more susceptible to a diverse array of assaults. This consequently puts at risk the integrity of energy data as well as the safety of energy management activities that depend on those data. This study offers a generative federated learning framework for semi-supervised threat detection in an IoT-assisted smart grid system. We refer to this framework as FSEI-Net. A unique semi-supervised edge intelligence network (SEI-Net) is presented in the FSEI-Net to enable semi-supervised training using labeled and unlabeled data in the edge tier. The design of SEI-Net is based on with bidirectional generative convolutional network that can intelligently capture the patterns of threat data from partially labeled smart grid data. We present federated training to enable remote edge servers to work together on training a semi-supervised detector without disclosing their own private local data. This is accomplished through cooperative training. To facilitate communication between cloud and edge layers that is both secure and respectful of users' privacy, a reputation-based block chain is introduced in the FSEI-Net. The outcomes from the practical applications demonstrate that the effectiveness of the proposed FSEI-Net over the most recent cutting-edge detection approaches are valid
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Karthik, M., and M. Krishnan. "Securing an Internet of Things from Distributed Denial of Service and Mirai Botnet Attacks Using a Novel Hybrid Detection and Mitigation Mechanism." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 113–23. http://dx.doi.org/10.22266/ijies2021.0228.12.
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Internet of Things (IoT) has become more familiar in all applications and industrial fields such as medical, military, transportation, etc. It has some limitations because of the attack model in the transmission or communication channel. Moreover, one of the deadliest attacks is known as a Distributed Denial of Service Attack (DDoS). The Presence of DDoS in network layer cause huge damage in data transmission channel that ends in data loss or collapse. To address this issue the current research focused on an innovative detection and mitigation of Mirai and DDoS attack in IoT environment. Initially, number of IoT devices is arranged with the help of a novel Hybrid Strawberry and African Buffalo Optimization (HSBABO). Consequently, the types of DDoS attacks are launched in the developed IoT network. Moreover, the presence of strawberry and African Buffalo fitness is utilized to detect and specify the attack types. Subsequently a novel MCELIECE encryption with Cloud Shield scheme is developed to prevent the low and high rate DDoS attack in the Internet of Things. Finally, the proposed model attained 94% of attack detection accuracy, 3% of false negative rate and 5.5% of false positive rate.
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Huda, Shamsul, John Yearwood, Mohammad Mehedi Hassan, and Ahmad Almogren. "Securing the operations in SCADA-IoT platform based industrial control system using ensemble of deep belief networks." Applied Soft Computing 71 (October 2018): 66–77. http://dx.doi.org/10.1016/j.asoc.2018.06.017.
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Muridzi, Gibson. "Implication of internet of things (IoT) on organisational performance for SMEs in emerging economies – a systematic review." Technology audit and production reserves 6, no. 4(74) (December 2, 2023): 27–35. http://dx.doi.org/10.15587/2706-5448.2023.292183.
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The object of this research is the implementation of Internet of Things (IoT) and its effect on organizational performance for Small and Medium Enterprises (SMEs) in emerging economies. SMEs in emerging economies are faced with low level of performance due to technological constraint, inadequate skilled human resource, lower entrepreneurial capabilities and management systems, the deficiency of available information, inadequate use of Information Technology, poor quality products and lack of strategic long-term plans. Systematic literature reviews approach was used to discover, assess and synthesize findings of all relevant individual research on fourth industrial revolution (4IR), IoT, organization performance and SMEs topics. 461 articles were found, and 60 articles were used as sample of this study. Findings of this study established that implementation of IoT positively affects performance for SMEs in emerging economies. Results also shows that financial, technological, and operational risks are major risks faced by SMEs in implementing IoT technologies in emerging economies. The essence of the results is to contribute to current body of knowledge by giving interesting insights in the form of a framework on how IoT technologies can be applied in enhancing SMEs performance in emerging economies. This is achieved by making SMEs aware of its potential benefit by providing some knowledge on securing financial resources, and ability to analyse external environment, and to shed more light on the benefits and opportunities that these new tools offer and how it can subsequently improve organization performance for SMEs. Most articles used systematic literature review were from developed economies as there was limited literature which speaks about IoT and SMEs performance in emerging economies. The study therefore focused on IoT and how it can improve SMEs’ organizational performance in emerging economies.
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Preuveneers, Davy, Wouter Joosen, and Elisabeth Ilie-Zudor. "Trustworthy data-driven networked production for customer-centric plants." Industrial Management & Data Systems 117, no. 10 (December 4, 2017): 2305–24. http://dx.doi.org/10.1108/imds-10-2016-0419.
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Purpose Industry 4.0 envisions a future of networked production where interconnected machines and business processes running in the cloud will communicate with one another to optimize production and enable more efficient and sustainable individualized/mass manufacturing. However, the openness and process transparency of networked production in hyperconnected manufacturing enterprises pose severe cyber-security threats and information security challenges that need to be dealt with. The paper aims to discuss these issues. Design/methodology/approach This paper presents a distributed trust model and middleware for collaborative and decentralized access control to guarantee data transparency, integrity, authenticity and authorization of dataflow-oriented Industry 4.0 processes. Findings The results of a performance study indicate that private blockchains are capable of securing IoT-enabled dataflow-oriented networked production processes across the trust boundaries of the Industry 4.0 manufacturing enterprise. Originality/value This paper contributes a decentralized identity and relationship management for users, sensors, actuators, gateways and cloud services to support processes that cross the trust boundaries of the manufacturing enterprise, while offering protection against malicious adversaries gaining unauthorized access to systems, services and information.
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Cultice, Tyler, Joseph Clark, Wu Yang, and Himanshu Thapliyal. "A Novel Hierarchical Security Solution for Controller-Area-Network-Based 3D Printing in a Post-Quantum World." Sensors 23, no. 24 (December 17, 2023): 9886. http://dx.doi.org/10.3390/s23249886.
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As the popularity of 3D printing or additive manufacturing (AM) continues to increase for use in commercial and defense supply chains, the requirement for reliable, robust protection from adversaries has become more important than ever. Three-dimensional printing security focuses on protecting both the individual Industrial Internet of Things (I-IoT) AM devices and the networks that connect hundreds of these machines together. Additionally, rapid improvements in quantum computing demonstrate a vital need for robust security in a post-quantum future for critical AM manufacturing, especially for applications in, for example, the medical and defense industries. In this paper, we discuss the attack surface of adversarial data manipulation on the physical inter-device communication bus, Controller Area Network (CAN). We propose a novel, hierarchical tree solution for a secure, post-quantum-supported security framework for CAN-based AM devices. Through using subnet hopping between isolated CAN buses, our framework maintains the ability to use legacy or third-party devices in a plug-and-play fashion while securing and minimizing the attack surface of hardware Trojans or other adversaries. The results of the physical implementation of our framework demonstrate 25% and 90% improvement in message costs for authentication compared to existing lightweight and post-quantum CAN security solutions, respectively. Additionally, we performed timing benchmarks on the normal communication (hopping) and authentication schemes of our framework.
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Charmanas, Konstantinos, Konstantinos Georgiou, Nikolaos Mittas, and Lefteris Angelis. "Classifying the Main Technology Clusters and Assignees of Home Automation Networks Using Patent Classifications." Computers 12, no. 10 (October 20, 2023): 211. http://dx.doi.org/10.3390/computers12100211.
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Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. In recent years, given the development of the Internet of Things (IoT) and artificial intelligence (AI) sectors, these technologies have seen a tremendous rise, both in the methodologies utilized and in their industrial impact. Hence, many organizations and companies are securing commercial rights by patenting such technologies. In this study, we employ an analysis of 8482 home automation patents from the United States Patent and Trademark Office (USPTO) to extract thematic clusters and distinguish those that drive the market and those that have declined over the course of time. Moreover, we identify prevalent competitors per cluster and analyze the results under the spectrum of their market impact and objectives. The key findings indicate that home automation networks encompass a variety of technological areas and organizations with diverse interests.
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Khurshid, Anum, Sileshi Demesie Yalew, Mudassar Aslam, and Shahid Raza. "TEE-Watchdog: Mitigating Unauthorized Activities within Trusted Execution Environments in ARM-Based Low-Power IoT Devices." Security and Communication Networks 2022 (May 25, 2022): 1–21. http://dx.doi.org/10.1155/2022/8033799.
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Trusted execution environments (TEEs) are on the rise in devices all around us ranging from large-scale cloud-based solutions to resource-constrained embedded devices. With the introduction of ARM TrustZone-M, hardware-assisted trusted execution is now supported in IoT nodes. TrustZone-M provides isolated execution of security-critical operations and sensitive data-generating peripherals. However, TrustZone-M, like all other TEEs, does not provide a mechanism to monitor operations in the trusted areas of the device and software in the secure areas of an IoT device has access to the entire secure and nonsecure software stack. This is crucial due to the diversity of device manufacturers and component suppliers in the market, which manifests trust issues, especially when third-party peripherals are incorporated into a TEE. Compromised TEEs can be misused for industrial espionage, data exfiltration through system backdoors, and illegal data sharing. It is of utmost importance here that system peripheral behaviour in terms of resource access is in accordance with their intended usage that is specified during integration. We propose TEE-Watchdog, a lightweight framework that establishes MPU protections for secure system peripherals in TrustZone-enabled low-end IoT devices. TEE-Watchdog ensures blocking unauthorized peripheral accesses and logging of application misbehaviour running in the TEE based on a manifest file. We define lightweight specifications and structure for the application manifest file enlisting permissions for critical system peripherals using concise binary object representation (CBOR). We implement and evaluate TEE-Watchdog using a Musca-A2 test chipboard. Our microbenchmark evaluations on CPU time and RAM usage demonstrated the practicality of TEE-Watchdog. Securing the system peripherals using TEE-Watchdog protections induced a 1.4% overhead on the latency of peripheral accesses, which was 61 microseconds on our test board. Our optimized CBOR-encoded manifest file template also showed a decrease in manifest file size by 40% as compared to the standard file formats, e.g., JSON.
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.., Anil Audumbar, Saurabh .., Hemachandran .., Shraddhesh Gadilkar, Zakka Benisemeni Esther, Ganesh Shivaji Pise, and Jude Imuede. "Utilizing Asymmetric Cryptography and Advanced Hashing Algorithms for Securing Communication Channels in IoT Networks Against Cyber Espionage." Journal of Cybersecurity and Information Management 13, no. 1 (2024): 46–59. http://dx.doi.org/10.54216/jcim.130105.
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This article describes a massive cryptographic scheme that can safeguard IoT communication paths. A combination of algorithms makes the technique operate. Communication security is handled differently by each algorithm. Elliptic Curve Cryptography (ECC), SHA-256 Secure Data Hashing, HMAC Message Authentication, and Merkle Tree Structures Decryption and Verification are used. Ablation is used to determine how each strategy increases security. The paper emphasizes that the algorithms function effectively together, demonstrating their importance for cyberdefense and surveillance. The recommended strategy is evaluated and found to operate better across key parameters.Top of Form
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Abosata, Nasr, Saba Al-Rubaye, Gokhan Inalhan, and Christos Emmanouilidis. "Internet of Things for System Integrity: A Comprehensive Survey on Security, Attacks and Countermeasures for Industrial Applications." Sensors 21, no. 11 (May 24, 2021): 3654. http://dx.doi.org/10.3390/s21113654.
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The growth of the Internet of Things (IoT) offers numerous opportunities for developing industrial applications such as smart grids, smart cities, smart manufacturers, etc. By utilising these opportunities, businesses engage in creating the Industrial Internet of Things (IIoT). IoT is vulnerable to hacks and, therefore, requires various techniques to achieve the level of security required. Furthermore, the wider implementation of IIoT causes an even greater security risk than its benefits. To provide a roadmap for researchers, this survey discusses the integrity of industrial IoT systems and highlights the existing security approaches for the most significant industrial applications. This paper mainly classifies the attacks and possible security solutions regarding IoT layers architecture. Consequently, each attack is connected to one or more layers of the architecture accompanied by a literature analysis on the various IoT security countermeasures. It further provides a critical analysis of the existing IoT/IIoT solutions based on different security mechanisms, including communications protocols, networking, cryptography and intrusion detection systems. Additionally, there is a discussion of the emerging tools and simulations used for testing and evaluating security mechanisms in IoT applications. Last, this survey outlines several other relevant research issues and challenges for IoT/IIoT security.
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Tarrés-Puertas, Marta I., Lluís Brosa, Albert Comerma, Josep M. Rossell, and Antonio D. Dorado. "Architecting an Open-Source IIoT Framework for Real-Time Control and Monitoring in the Bioleaching Industry." Applied Sciences 14, no. 1 (December 29, 2023): 350. http://dx.doi.org/10.3390/app14010350.
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Electronic waste (e-waste) contains toxic elements causing an important impact on environmental and human health. However, the presence of valuable metals, such as copper or gold, among others, make recycling a necessity for obtaining an alternative source of raw materials. Conventional metal recovery methods are environmentally unsound, prompting the exploration of greener alternatives like bioleaching, which utilizes the activity of microorganisms for a more sustainable recovery. However, the mechanisms involved in the process and the conditions to optimize the metabolic paths are still not completely known. Monitorization and automatization of the different stages composing the global process are crucial for advancing in the implementation of this novel technology at an industrial scale. For the first time, an open-source industrial IoT system is designed to enhance and regulate bioleaching by implementing real-time monitoring and control within the plant’s infrastructure. This system includes an Android app that displays real-time plant data from sensors and a robust server featuring a flexible application programming interface (API) for future applications. The app caters to specific needs such as remote sensor reading, actuator control, and real-time bioleaching alerts, ensuring secure access and proactive event management. By utilizing collected data, it minimizes downtime, equipment failures, and supply chain disruptions. The server maintains seamless communication with the plant controller, enabling efficient pump activation and sensor data transmission. A telegram bot demonstrates the API’s flexibility by forwarding plant alerts to users. During validation with concurrent remote user access, the application demonstrated its ability to prevent irreversible plant failures through an advanced alarm system. Ultimately, this IIoT system amplifies plant performance, safety, and efficiency by optimizing processes and decision-making capabilities. It emerges as a pivotal open-source tool, securing remote oversight and management of large-scale bioleaching plants, promising adaptability for future enhancements.
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Raimundo, Ricardo Jorge, and Albérico Travassos Rosário. "Cybersecurity in the Internet of Things in Industrial Management." Applied Sciences 12, no. 3 (February 2, 2022): 1598. http://dx.doi.org/10.3390/app12031598.
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Nowadays, people live amidst the smart home domain, while there are business opportunities in industrial smart cities and healthcare. However, there are concerns about security. Security is central for IoT systems to protect sensitive data and infrastructure, whilst security issues have become increasingly expensive, in particular in Industrial Internet of Things (IIoT) domains. Nonetheless, there are some key challenges for dealing with those security issues in IoT domains: Applications operate in distributed environments such as Blockchain, varied smart objects are used, and sensors are limited, as far as machine resources are concerned. In this way, traditional security does not fit in IoT systems. The issue of cybersecurity has become paramount to the Internet of Things (IoT) and the Industrial Internet of Things (IIoT) in mitigating cybersecurity risk for organizations and end users. New cybersecurity technologies/applications present improvements for IoT security management. Nevertheless, there is a gap in the effectiveness of IoT cyber risk solutions. This review article discusses the literature trends around opportunities and threats in cybersecurity for IIoT, by reviewing 70 key articles discovered from a profound Scopus literature survey. It aims to present the current debate around the issue of IIoT rather than suggesting any particular technical solutions to solve network security problems.
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Shevtsov, Vadim, and Nikita Kasimovsky. "Threat and Vulnerability Analysis of IoT and IIoT Concepts." NBI Technologies, no. 3 (March 2021): 28–35. http://dx.doi.org/10.15688/nbit.jvolsu.2020.3.5.
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IoT and IIoT are new information technologies. They are very efficient solutions for home, industry and infrastructure. A lot of complex processes can be implemented using this systems. The popularity of the industrial Internet of things is steadily growing along with the development of the Internet of things. Both of these approaches involve the exchange of data over the Internet, use of common hardware platforms and are managed by using specialized software, and this leads to an increase in the number of common vulnerabilities and possible attacks on industrial facilities. The Frost & Sullivan report shows that industrial and IT infrastructures are becoming more transparent. First of all, this is due to the development of the Industrial 4.0 standard and the refusal to isolate industrial facilities, which entails common vulnerabilities, the use of security services based on the SaaS model for industrial facilities, as well as the use of hardware devices that a potential attacker can access quite easily. But very actual problems of IoT and IIoT are information security. Many of this systems are critical and little error can stop the entire system. This is not hard for hackers because that complex system has sensitive components usually. For example simple router can have a lot of vulnerabilities. There an attacker takes a root easily in every system. To solve the problem successfully it is recommended to use complex security actions. These are secure configurations of network devices, using safe devices and protocols, regular audit, using backups, using actual politics of information security.
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Alasmary, Hisham. "RDAF-IIoT: Reliable Device-Access Framework for the Industrial Internet of Things." Mathematics 11, no. 12 (June 15, 2023): 2710. http://dx.doi.org/10.3390/math11122710.
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The Internet of Things (IoT) has experienced significant growth and is now a fundamental part of the next-generation Internet. Alongside improving daily life, IoT devices generate and collect vast amounts of data that can be leveraged by AI-enabled big data analytics for diverse applications. However, due to the machine-to-machine communication inherent in IoT, ensuring data security and privacy is crucial to mitigate various malicious cyber attacks, including man-in-the-middle, impersonation, and data poisoning attacks. Nevertheless, designing an efficient and adaptable IoT security framework poses challenges due to the limited computational and communication power of IoT devices, as well as their wide-ranging variety. To address these challenges, this paper proposes an Access Key Agreement (AKA) scheme called the “Reliable Device-Access Framework for the Industrial IoT (RDAF-IIoT)”. RDAF-IIoT verifies the user’s authenticity before granting access to real-time information from IIoT devices deployed in an industrial plant. Once authenticated at the gateway node, the user and IIoT device establish a session key for future encrypted communication. The security of the proposed RDAF-IIoT is validated using a random oracle model, while the Scyther tool is employed to assess its resilience against various security attacks. Performance evaluations demonstrate that the proposed scheme requires lower computational and communication costs compared to related security frameworks while providing enhanced security features.
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Kant, Daniel, Andreas Johannsen, and Reiner Creutzburg. "Analysis of IoT Security Risks based on the exposure of the MQTT Protocol." Electronic Imaging 2021, no. 3 (June 18, 2021): 96–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.3.mobmu-096.
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Due to the tremendous growth of Internet of Things (IoT) applications - e.g. smart homes, smart grids, smart factories – and the emerging integration into industrial systems, the cyber threat landscape for IoT and IIoT applications is rapidly evolving. Security by Design principles are still widely neglected in the design of IoT devices and protocols. For consumer IoT, the privacy of the applicant can be compromised when devices are inappropriately secured. With regard to Industrial IoT, the usage of insecure IIoT protocols such as MQTT can have a severe impact on the industrial environment such as failure or impairment of production systems. We evaluate the prevalence of exposed IoT and IIoT devices related to the protocol MQTT by means of the search engine Shodan. The approach, design and results of our analysis are summarized in this paper.
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Bhardwaj, Akashdeep, Keshav Kaushik, Salil Bharany, Ateeq Ur Rehman, Yu-Chen Hu, Elsayed Tag Eldin, and Nivin A. Ghamry. "IIoT: Traffic Data Flow Analysis and Modeling Experiment for Smart IoT Devices." Sustainability 14, no. 21 (November 7, 2022): 14645. http://dx.doi.org/10.3390/su142114645.
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The Internet of Things (IoT) has redefined several aspects of our daily lives, including automation and control of the living environment, innovative healthcare services, and much more. Digital IoT devices and sensors, when integrated with home appliances, industrial systems, and online services in the physical world, have brought intense, disruptive changes in our lives. The industry and home users have widely embraced these ‘things’ on the Internet or IoT. However, the innate, intrinsic repercussions regarding security and data privacy are not evaluated. Security applies to Industrial IoT (IIoT) is in its infancy stage. Techniques from security and privacy research promise to address broad security goals, but attacks continue to emerge in industrial devices. This research explores the vulnerabilities of IIoT ecosystems not just as individual nodes but as the integrated infrastructure of digital and physical systems interacting with the domains. The authors propose a unique threat model framework to analyze the attacks on IIoT application environments. The authors identified sensitive data flows inside the IIoT devices to determine privacy risks at the application level and explored the device exchanges at the physical level. Both these risks lead to insecure ecosystems. The authors also performed a security analysis of physical domains to digital domains.
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Farooq, Muhammad Shoaib, Muhammad Abdullah, Shamyla Riaz, Atif Alvi, Furqan Rustam, Miguel Angel López Flores, Juan Castanedo Galán, Md Abdus Samad, and Imran Ashraf. "A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry." Sensors 23, no. 21 (November 3, 2023): 8958. http://dx.doi.org/10.3390/s23218958.
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The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.
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Alotaibi, Bandar. "A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities." Sensors 23, no. 17 (August 28, 2023): 7470. http://dx.doi.org/10.3390/s23177470.
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The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected by an IoT network, along with a collection of enabling technologies, such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, there has been an inevitable increase in research proposals over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security.
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Varga, Pal, Jozsef Peto, Attila Franko, David Balla, David Haja, Ferenc Janky, Gabor Soos, Daniel Ficzere, Markosz Maliosz, and Laszlo Toka. "5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps." Sensors 20, no. 3 (February 4, 2020): 828. http://dx.doi.org/10.3390/s20030828.
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Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps.
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Alshahrani, Hani, Attiya Khan, Muhammad Rizwan, Mana Saleh Al Reshan, Adel Sulaiman, and Asadullah Shaikh. "Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network." Sustainability 15, no. 11 (June 2, 2023): 9001. http://dx.doi.org/10.3390/su15119001.
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The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework’s network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%.
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Vijayakumaran, C., B. Muthusenthil, and B. Manickavasagam. "A reliable next generation cyber security architecture for industrial internet of things environment." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (February 1, 2020): 387. http://dx.doi.org/10.11591/ijece.v10i1.pp387-395.
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Architectural changes are happening in the modern industries due to the adaption and the deployment of ‘Internet of Things (IoT)’ for monitoring and controlling various devices remotely from the external world. The most predominant place where the IoT technology makes the most sense is the industrial automation processes in smart industries (Industry 4.0). In this paper, a reliable ‘Next Generation Cyber Security Architecture (NCSA)’ is presented for Industrial IoT (IIoT) environment that detects and thwarts cybersecurity threats and vulnerabilities. It helps to automate the processes of exchanging real-time critical information between devices without any human intervention. It proposes an analytical framework that can be used to protect entities and network traffics involved in the IIoT wireless communication. It incorporates an automated cyber-defense authentication mechanism that detects and prevents security attacks when a network session has been established. The defense mechanism accomplishes the required level of security protection in the network by generating an identity token which is cryptographically encrypted and verified by a virtual gateway system. The proposed NCSA improves security in the IIoT environment and reduces operational management cost.
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Ullah, Insaf, Ali Alkhalifah, Maha M. Althobaiti, Fahd N. Al-Wesabi, Anwer Mustafa Hilal, Muhammad Asghar Khan, and Jimmy Ming-Tai Wu. "Certificate-Based Signature Scheme for Industrial Internet of Things Using Hyperelliptic Curve Cryptography." Wireless Communications and Mobile Computing 2022 (February 8, 2022): 1–8. http://dx.doi.org/10.1155/2022/7336279.
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The Industrial Internet of Things (IIoT) is a technology that uses the Internet of Things (IoT) infrastructure to sense, process, and communicate real-time events in the industrial system to cut down on unnecessary operating costs and to speed up industrial automation of internal and external working processes. Since the IIoT system inherits the same cyber-physical vulnerabilities that the IoT system already encounters, it requires additional work to address security concerns owing to its heterogeneous nature. As a result, an efficient security mechanism is essential to protect against various and unknown cyber-attacks. In this article, we propose a certificate-based signature scheme based on hyperelliptic curve cryptography (HECC), with the aim of improving security while reducing computational and communication costs in the IIoT environment. The proposed scheme outperforms existing schemes in terms of both computational and communication costs, as well as offering better security.
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Dwivedi, Sanjeev Kumar, Priyadarshini Roy, Chinky Karda, Shalini Agrawal, and Ruhul Amin. "Blockchain-Based Internet of Things and Industrial IoT: A Comprehensive Survey." Security and Communication Networks 2021 (August 23, 2021): 1–21. http://dx.doi.org/10.1155/2021/7142048.
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Industry 4.0 connects the latest technologies such as cloud computing, Internet of things (IoT), machine learning and artificial intelligence (ML/AI), and blockchain to provide more automation in the industrial process and also bridges the gap between the physical and digital worlds through the cyber-physical system. The inherent feature of IoT devices creates the industry to smart industry (referred to as industrial IoT, i.e., IIoT) through its data-driven decision policies. However, several challenges such as decentralization, security and privacy vulnerability, single point of failure (SPOF), and trust issues exist in the IoT system. Blockchain is one of the promising technologies that can bring about opportunities for addressing the challenges of IoT systems. In this article, we have investigated the integration of IoT with blockchain technology and provided an in-depth study of the blockchain-enabled IoT and IIoT systems. The state-of-the-art research is categorized into data storage and management technique, big data and cloud computing technique (finance and data auditing), and industrial sectors (supply chain, energy, and healthcare sector). The insightful discussion based on the different categories is also presented in the paper. In particular, first, we introduce the IoT and IIoT and then discuss the need for smart contracts in IoT and IIoT systems. Next, we concentrate on the convergence of blockchain and IoT with state-of-the-art research. In addition, this article also provides the open and future research directions towards this era with the highlighted observations.
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Essop, Ismael, José C. Ribeiro, Maria Papaioannou, Georgios Zachos, Georgios Mantas, and Jonathan Rodriguez. "Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks." Sensors 21, no. 4 (February 23, 2021): 1528. http://dx.doi.org/10.3390/s21041528.
Full textAbstract:
Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin “powertrace” and the Cooja tool “Radio messages”.
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Ma, Jinnan, Xuekui Shangguan, and Ying Zhang. "IoT Security Review: A Case Study of IIoT, IoV, and Smart Home." Wireless Communications and Mobile Computing 2022 (August 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6360553.
Full textAbstract:
The Internet of Things (IoT) acts as a tremendous network that is constructed by fusing diverse sensors. IoT can achieve the interconnection of individuals, things, and machines at any place and time and improve the function performance of network applications. However, the security of IoT has always been a major problem that may limit the application perspective of IoT technologies. Nowadays, industrial IOT (IIoT), Internet of vehicles (IoV), and smart home have become the three primary emerging perspectives of the current IoT studies, and it is necessary to systematically highlight the security analysis of these three types of scenarios. Hence, in this paper, guided by the three major IoT application scenarios, i.e., IIoT, IoV, and smart home, we sum up the development status of IoT security technologies, analyzed corresponding technical difficulties, and discussed several future outlook of challenges and development trends for the IoT technology.