Industrial Internet Of Things Security Research Articles

ENHANCING IIOT SECURITY WITH MACHINE LEARNING AND DEEP LEARNING FOR INTRUSION DETECTION

The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well-optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, man-in-the-middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (k-nearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks.

Combatting ransomware in ZephyrOS-activated industrial IoT environments

The rapid growth of the Industrial Internet of Things (IIoT) has opened up new avenues for cyber threats, with ransomware being a primary area of concern. In response to this, proposed study introduces an innovative approach that combines the strength of the Gradient Boosting Machine (GBM) and the precision of Lasso Regression to effectively identify ransomware threats in IIoT settings. Functioning on the Zephyr operating system, the GBM's ability to handle large-scale datasets and traverse complex data dimensions is complemented by Lasso Regression's skill in curbing overfitting and extracting critical features. This combined ML technique is specifically designed to address the diverse data challenges of IIoT, providing a solid line of defense. Comprehensive tests on updated ransomware tools and the established RanSAP & IoT-23 datasets validated our model's capabilities, achieving an impressive 92 percent detection rate while keeping false positives to a minimum. When compared to existing strategies, projected solution showcased superior performance, highlighting its pivotal role in bolstering IIoT security against ransomware attacks. These results shed light on the next steps for ensuring a safer IIoT landscape, emphasizing the need for advanced, flexible cybersecurity measures in our ever-evolving industrial ecosystem.

ASAP-IIOT: An Anonymous Secure Authentication Protocol for Industrial Internet of Things.

With the increasing demand for a digital world, the Industrial Internet of Things (IIoT) is growing rapidly across various industries. In manufacturing, particularly in Industry 4.0, the IIoT assumes a vital role. It encompasses many devices such as sensing devices, application servers, users, and authentication servers within workshop settings. The security of the IIoT is a critical issue due to wireless networks' open and dynamic nature. Therefore, designing secure protocols among those devices is an essential aspect of IIoT security functionality and poses a significant challenge to the IIoT systems. In this paper, we propose a lightweight anonymous authentication protocol to preserve privacy for IIoT users, enabling secure IIoT communication. The protocol has been validated to demonstrate its comprehensive ability to overcome various vulnerabilities and prevent malicious attacks. Finally, the performance evaluation confirms that the proposed protocol is more effective and efficient than the existing alternatives.

A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario

A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario

A Comprehensive Survey of Cybersecurity Threats, Attacks, and Effective Countermeasures in Industrial Internet of Things

The Industrial Internet of Things (IIoT) ecosystem faces increased risks and vulnerabilities due to adopting Industry 4.0 standards. Integrating data from various places and converging several systems have heightened the need for robust security measures beyond fundamental connection encryption. However, it is difficult to provide adequate security due to the IIoT ecosystem’s distributed hardware and software. The most effective countermeasures must be suggested together with the crucial vulnerabilities, linked threats, and hazards in order to protect industrial equipment and ensure the secure functioning of IIoT systems. This paper presents a thorough analysis of events that target IIoT systems to alleviate such concerns. It also offers a comprehensive analysis of the responses that have been advanced in the most recent research. This article examines several kinds of attacks and the possible consequences to understand the security landscape in the IIoT area. Additionally, we aim to encourage the development of effective defenses that will lessen the hazards detected and secure the privacy, accessibility, and reliability of IIoT systems. It is important to note that we examine the issues and solutions related to IIoT security using the most recent findings from research and the literature on this subject. This study organizes and evaluates recent research to provide significant insight into the present security situation in IIoT systems. Ultimately, we provide outlines for future research and projects in this field.

Cybersecurity Risk Analysis in the IoT: A Systematic Review

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

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations. However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson's Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18s and 6.25s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.

A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities.

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.

Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey

The industrial internet of things (IIoT) is a key pillar of the intelligent society, integrating traditional industry with modern information technology to improve production efficiency and quality. However, the IIoT also faces serious challenges from advanced persistent threats (APTs), a stealthy and persistent method of attack that can cause enormous losses and damages. In this paper, we give the definition and development of APTs. Furthermore, we examine the types of APT attacks that each layer of the four-layer IIoT reference architecture may face and review existing defense techniques. Next, we use several models to model and analyze APT activities in IIoT to identify their inherent characteristics and patterns. Finally, based on a thorough discussion of IIoT security issues, we propose some open research topics and directions.

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

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%.

Research on Industrial IoT Security Based on Deep Learning

<p>With the rapid development of "Internet +" and the construction of a new generation of information infrastructure, the intrusion behaviors against the Industrial Internet of Things are increasingly common. How to ensure the security of the industrial Internet of Things is one of the current research hotspots. The modern technology trend has the hottest technologies of the Internet of Things (IoT). The application of IoT on the other hand improves work efficiency and brings convenience to people’s life; on the other hand, it makes the network face increasingly serious security threat problems and attacks the network by unscrupulous elements occur from time to time. Machine learning-based intrusion detection techniques involve a large number of mathematical formula operations, while with the development of neural networks, the excellent autonomous feature learning capability of deep learning is recognized. An intrusion detection system plays an important role in preventing security threats and protecting them from attacks. The current research on industrial IoT security technology focuses on authentication technology, encryption technology, access control technology, and intrusion detection technology. In this paper, we analyze deep learning and industrial IoT intrusion detection and use the powerful data processing capability and feature learning capability of deep learning to conduct an in-depth study on industrial IoT intrusion detection methods based on deep learning. This paper achieves a 96.32% detection rate on industrial control dataset, which can better adapt to the needs of industrial IoT intrusion detection.</p> <p> </p>

SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet‐of‐Things

AbstractIn recent years, deep learning has been applied to a variety of scenarios in Industrial Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the designers. The authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper‐parameters optimisation for securing IIoT. A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks (SOPA‐GA‐CNN) is proposed to synchronously optimise the hyperparameters and block‐based architectures in convolutional neural networks (CNNs) by genetic algorithms (GA) for the intrusion detection issue of IIoT. An efficient hybrid encoding strategy and the corresponding GA‐based evolutionary operations are designed to characterise and evolve both the hyperparameters, including batch size, learning rate, weight optimiser and weight regularisation, and the architectures, such as the block‐based network topology and the parameters of each CNN block. The experimental results on five intrusion detection datasets in IIoT, including secure water treatment, water distribution, Gas Pipeline, Botnet in Internet of Things and Power System Attack Dataset, have demonstrated the superiority of the proposed SOPA‐GA‐CNN to the state‐of‐the‐art manually designed models and neuron‐evolutionary methods in terms of accuracy, precision, recall, F1‐score, and the number of parameters of the deep learning models.

ИССЛЕДОВАНИЕ МЕТОДОВ АУТЕНТИФИКАЦИИ В ПРОМЫШЛЕННОМ ИНТЕРНЕТЕ ВЕЩЕЙ

One of the most critical and key issues in the field of the Industrial Internet of Things security is the authentication. The main aim of this paper is to identify possible methods of solving authentication issues. The relevance of the research is confirmed by the “Information Security Doctrine of the Russian Federation” and emerging risks caused by information and technical impact on industrial facilities in the current geopolitical environment. Based on the presented literature review, the architecture of the industrial Internet of things, options for protecting authentication protocols from known attacks, as well as options for protection using blockchain technology are presented. When analyzing network models, it was revealed that information protection options do not depend on the model itself, but depend on the architectural level at which the protection of a particular model is built. The paper discusses the advantages and disadvantages of various protection options that should be considered during its implementation. The obtained results can be used to select a method for protecting the authentication protocol when building an information system in the field of the Internet of things.

A Hybrid Optimization-Based Medical Data Hiding Scheme for Industrial Internet of Things Security

With the development of the industrial internet technology, the medical data exchange in IoT systems has become more prosperous. Specially, more and more medical images produced by industrial and intelligent devices are outsourced to the cloud for convenient use. However, IoT systems deployment poses several medical data security challenges. To address this issue, in this article, a robust medical data hiding scheme based on secure hybrid optimization for industrial scenario image is presented. Specifically, the marked image is obtained through non-subsampled shearlet transform-multiresolution singular value decomposition. In order to generate the dual marks, we employ the Fisher–Yates permutation to produce the scrambled system watermark address for embedding into the mark image. Afterward, the generated mark image is embedded in the chosen coefficients of the cover in an invisible way. After the watermarking, a hybrid optimization-based encryption scheme is utilized to secure the marked image. Extensive experiments demonstrate the invisibility, security, and robustness of our scheme. Further, the superiority of the scheme is elaborated through making the comparison with the other similar algorithms. The solution not only performs the robust exchange of medical data but also protects the privacy of patients.

An effective intrusion detection approach based on ensemble learning for IIoT edge computing

The Internet of Things (IoT) has recently gained immense popularity. IoT is a set of devices that compute, communicate with each other, and consist of a vast network. Industrial IoT (IIoT) is the extension of IoT in the industrial fields. It aims to involve embedded devices in the industrial sectors to improve their activities. However, IIoT raises security vulnerabilities that are more harmful than those of IoT. Thus, intrusion detection systems (IDS) are developed to prevent some devastating intrusions. IDS monitors the environment to detect intrusion in real time. This paper designs an intrusion detection approach using ML for IIoT security. The feature selection and dimensionality reduction methods promote the machine learning models' detection rate and accuracy (ACC). To reduce the computational and time costs caused by the dataset's high dimensionality, we propose using Pearson's correlation coefficient (PCC) and isolation forest (IF). The IF is applied to remove outliers, and The PCC is implemented for the feature selection process. Additionally, we used Matthews correlation coefficient (MCC) to study the impact of our proposed model on the imbalanced dataset as the Bot-IoT. The RF classifier is implemented to enhance the IDS performances. For evaluation, we used the Bot-IoT and the wustl_iiot_2021 datasets to evaluate the performance of our model. Our approach has shown remarkable results with 99.99% and 99.12% ACC, 92.17% and 93.96% MCC, and 92.48% and 99.3% AUC scores on the Bot-IoT and wustl_iiot_2021, respectively. Our results demonstrate that the proposed approach has many advantages and superior performances compared with other models.

High-capacity reversible data hiding in encrypted images based on pixel-value-ordering and histogram shifting

New security threats are generated with the coming of information industrial 5.0 represented by artificial intelligence and Industrial Internet of Things (IIoT). One of the most significant concerns in IIoT is information security which refers to the protection of the privacy of image owner. To this end, a new reversible data hiding in encrypted image (RDHEI) method with high capacity for IIoT security is proposed in this paper. On image owner side, image contents are protected by a block level image encryption method while the correlations of adjacent pixels inside the same block are not damaged. On receiver side, to realize reversibility, all encrypted pixel blocks are classified into overflow blocks and non-overflow blocks and only non-overflow blocks are used to carry additional data. Then, some secret data, which is used to manage encrypted images, is embedded into the largest and smallest pixels in non-overflow blocks by utilizing pixel-value-ordering and histogram shifting techniques. On the receiver side, original image and secret data can be recovered perfectly. Moreover, according the availability of decryption key and data hiding key, image recovery and data extraction are separably executed. Experimental results demonstrate that significant improvement in embedding capacity is achieved without destroying reversibility. In this way, the proposed method outperforms several state-of-the-art methods and it is quite suitable for IIoT.

IIoT-IDS Network using Inception CNN Model

Modern network and Industrial Internet of Things (IIoT) technologies are quite advanced. Networks experience data breaches annually. As a result, an Intrusion Detection System is designed for enhancing the IIoT security protection under privacy laws. The Internet of Things' structural system and security performance criteria must meet high standards in an adversarial network. The network system must use a system that is very stable and has a low rate of data loss. The basic deep learning network technology is picked after analysing it with a huge number of other network configurations. Further, the network is upgraded and optimised by the Convolutional Neural Network technique. Additionally, an IIoT anti-intrusion detection system is built by combining three network technologies. The system's performance is evaluated and confirmed. The proposed model gives a better detection rate with a minimum false positive rate, and good data correctness. As a result, the proposed method can be used for securing an IIoT data privacy under the law.

Role of fog‐assisted Industrial Internet of Things: A systematic review

AbstractThe manufacturing industry is benefiting a lot since the Industrial Internet of Things (IIoT) deploys sensors and actuators to calibrate, audit, and analyze data to enhance efficiency and profitability, thereby automating systems in the industrial sector. The exponential growth of smart devices in the industrial sector requires real‐time connectivity, low latency, nearby storage, secure communication, and reliability. To meet this demand, fog computing is effectively used in IIoT applications. Fog computing solves most of the problems in the fourth industrial revolution, which is the interface between end‐user devices and cloud/web services. Fog also allows for local processing with minimal acceptable latency. This article focuses on how fog computing becomes part of IIoT and also explores the architecture of fog‐assisted IIoT applications. The taxonomy is built by classifying the existing literature to reveal the salient features of fog computing in IIoT. This article also discusses IIoT security solutions as well as research problems of fog‐assisted IIoT. This article also outlines the research challenges of fog‐assisted IIoT and also discusses the security solutions of IIoT.

Intrusion Detection System After Data Augmentation Schemes Based on the VAE and CVAE

Industrial Internet of Things (IoT) is the most rapidly developing industry in the current IoT industry, and the intrusion detection system (IDS) remains one of the key technologies for industrial IoT security protection. Researchers have considered applying algorithms such as machine learning and deep learning to network IDSs to cope with complex and changing network environments and to automatically extract key features from high-dimensional feature data. However, in the real industrial IoT environment, data imbalance is the main factor that affects the performance of the deep-learning-based IDS. In this article, we study the network intrusion detection model based on data level. Three data-based research schemes are constructed step by step in this article, which are a data augmentation scheme based on the variational autoencoder (VAE), a data-balancing scheme based on the conditional VAE, and a data-balancing scheme based on random undersampling and conditional VAE. The three data-level-based schemes are combined with the deep-learning-based IDS. In this article, we build experiments based on the CSE-CIC-IDS2018 dataset to verify the effectiveness of three data processing schemes. After data enhancement through the third scheme, the Macro-F1-score of the convolutional-neural-network-based IDS model improved by 3.75% and the Macro-F1-score of the gated-recurrent-unit-based IDS model improved by 5.32%.

Blockchain enabled optimal Hopfield Chaotic Neural network based secure encryption technique for industrial internet of things environment

Industrial Internet of Things (IIoT) denotes a network of interlinked sensors, instruments, and other devices for industrial applications in the domains of manufacturing, logistics, transportation, etc. IIoT security is a major crucial research area for several applications. Image encryption techniques gained popularity in the recent years, thanks to increasing requirements for secure image transmission in IIoT environments. At the same time, conventional security solutions built for sensitive data protection are getting outdated in IIoT environment due to the participation of third party. Blockchain (BC) is one of the recent solutions used for security purpose which eliminates the involvement of a third party. With this motivation, the current research article presents a new BC-Enabled Shark Smell Optimization with Hopfield Chaotic Neural Network (SSO-HCNN) for secure encryption in IoT environment. The proposed SSO-HCNN model exploits a composite Chaotic Map (CM) which is integrated into staged logistic and tent maps to initially process the images and develop the variables needed for Arnold mapping. In addition, the SSO algorithm is developed with maximum PSNR and coefficient fitness function to select the optimum secret and public keys of the system amongst the random numbers. Besides, the diffusion phase utilizes HCNN to create a self-diffusion chaotic matrix whereas the jumbled image performs XOR operation using the keys to obtain the cipher image. In SSO-HCNN model, the cryptographic pixel value in the image is saved on BC thus guaranteeing the security and privacy of the images. To examine the superior performance of SSO-HCNN model over state-of-the-art methods, a set of simulations was conducted on benchmark test images. The simulation results of the proposed SSO-HCNN model were promising under different evaluation parameters.