Industrial Internet Of Things Security Research Articles

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.

A Threat Intelligence Analysis Method Based on Feature Weighting and BERT-BiGRU for Industrial Internet of Things

The combination of 5G technology and the industrial Internet of things (IIoT) makes it possible to realize the interconnection of all things. Still, it also increases the risk of attacks such as large-scale DDoS attacks and IP spoofing attacks. Threat intelligence is a collection of information causing potential and nonpotential harm to the industrial Internet. Extracting network security entities and their relationships from threat intelligence text and constructing structured threat intelligence information are particularly important for IIoT security protection. However, threat intelligence is mostly text reports, which means the value information needs to be extracted manually by security analysts, and it is highly dependent on personnel experience. Therefore, this study proposes an IIoT threat intelligence analysis method based on feature weighting and BERT-BiGRU. In this method, BERT-BiGRU is used to classify attack behavior and attack strategy. Then, the attack behavior is weighted to make the classified result more accurate according to the relationship between attack strategy and attack behavior in ATT&CK for ICS knowledge. Finally, the possibility of attack and the harm degree of attack are calculated to form the threat value of the attack. The security analysts can judge the emergency response sequence by the threat value to improve the accuracy and efficiency of emergency response. The results indicate that the proposed method in this study is more accurate than the other standard methods and is more suitable for the unstructured threat intelligence analysis of IIoT.

A Lightweight Hybrid Intrusion Detection Framework using Machine Learning for Edge-Based IIoT Security

Due to the development of cloud computing and Internet of Things (IoT) environments, such as healthcare systems, telecommunications and Industry 4.0 or Industrial IoT (IIoT) many daily services are transformed. Therefore, Security issues become useful to better protect these novel technologies. IIoT security represents a real challenge for industry actors and academic research. A set of security approaches, such as intrusion detection are integrated to improve IIoT environments security. Hence, an Intrusion Detection System (IDS) aims to monitor, detect an intrusion in real time and then make reliable decisions. Many recent IDS incorporate Machine Learning (ML) techniques to improve their Accuracy (ACC), precision and Detection Rate (DR). This paper presents a hybrid IDS for Edge-Based IIoT Security using ML techniques. This new hybrid framework is based on misuse and anomaly detection using K-Nearest Neighbor (K-NN) and Principal Component Analysis (PCA) techniques. Specifically, the K-NN classifier has been incorporated to improve detection accuracy and make effective decision and the PCA is used for an enhanced feature engineering and training process. The obtained results have proven that our proposed Framework presents many advantages compared with other recent models. It gives good results with 99.10% ACC, 98.4% DR 2.7% False Alarm Rate (FAR) on NSL-KDD dataset and 98.2% ACC, 97.6% DR, 2.9% FAR on Bot-IoT dataset.

Analysis of Security Issues and Countermeasures for the Industrial Internet of Things

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.

Recent Technologies, Security Countermeasure and Ongoing Challenges of Industrial Internet of Things (IIoT): A Survey.

The inherent complexities of Industrial Internet of Things (IIoT) architecture make its security and privacy issues becoming critically challenging. Numerous surveys have been published to review IoT security issues and challenges. The studies gave a general overview of IIoT security threats or a detailed analysis that explicitly focuses on specific technologies. However, recent studies fail to analyze the gap between security requirements of these technologies and their deployed countermeasure in the industry recently. Whether recent industry countermeasure is still adequate to address the security challenges of IIoT environment are questionable. This article presents a comprehensive survey of IIoT security and provides insight into today’s industry countermeasure, current research proposals and ongoing challenges. We classify IIoT technologies into the four-layer security architecture, examine the deployed countermeasure based on CIA+ security requirements, report the deficiencies of today’s countermeasure, and highlight the remaining open issues and challenges. As no single solution can fix the entire IIoT ecosystem, IIoT security architecture with a higher abstraction level using the bottom-up approach is needed. Moving towards a data-centric approach that assures data protection whenever and wherever it goes could potentially solve the challenges of industry deployment.

A Novel Image Steganography Method for Industrial Internet of Things Security

The rapid development of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) brings new security threats by exposing secret and private data. Thus, information security has become a major concern in the communication environment of IIoT and AI, where security and privacy must be ensured for the messages between a sender and the intended recipient. In this article, we propose a method called Harris hawks optimization-integer wavelet transform (HHO-IWT) for covert communication and secure data in the IIoT environment based on digital image steganography. The method embeds secret data in the cover images using a metaheuristic optimization algorithm called HHO to efficiently select image pixels that can be used to hide bits of secret data within integer wavelet transforms. The HHO-based pixel selection operation uses an objective function evaluation depending on the following two phases: exploitation and exploration. The objective function is employed to determine an optimal encoding vector to transform secret data into an encoded form generated by the HHO algorithm. Several experiments are conducted to validate the performance of the proposed method with respect to visual quality, payload capacity, and security against attacks. The obtained results reveal that the HHO-IWT method achieves higher levels of security than the state-of-the-art methods and that it resists various forms of steganalysis. Thus, utilizing this approach can keep unauthorized individuals away from the transmitted information and solve some security challenges in the IIoT.

Industrial Internet-of-Things Security Enhanced With Deep Learning Approaches for Smart Cities

The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning (DL) techniques for IIoT in smart cities, mainly deep reinforcement learning, recurrent neural networks, and convolutional neural networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying DL techniques to enhance IIoT security.

Developing a Security Testbed for Industrial Internet of Things

While achieving security for Industrial Internet of Things (IIoT) is a critical and nontrivial task, more attention is required for brownfield IIoT systems. This is a consequence of long life cycles of their legacy devices which were initially designed without considering security and IoT connectivity, but they are now becoming more connected and integrated with emerging IoT technologies and messaging communication protocols. Deploying today's methodologies and solutions in brownfield IIoT systems is not viable, as security solutions must co-exist and fit these systems' requirements. This necessitates a realistic standardized IIoT testbed that can be used as an optimal format to measure the credibility of security solutions of IIoT networks, analyze IIoT attack landscapes and extract threat intelligence. Developing a testbed for brownfield IIoT systems is considered a significant challenge as these systems are comprised of legacy, heterogeneous devices, communication layers and applications that need to be implemented holistically to achieve high fidelity. In this article, we propose a new generic end-to-end IIoT security testbed, with a particular focus on the brownfield system and provide details of the testbed's architectural design and the implementation process. The proposed testbed can be easily reproduced and reconfigured to support the testing activities of new processes and various security scenarios. The proposed testbed operation is demonstrated on different connected devices, communication protocols and applications. The experiments demonstrate that this testbed is effective in terms of its operation and security testing. A comparison with existing testbeds, including a table of features is provided.

Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions

AbstractIn a wireless Industrial Internet of Things (IIoT) network, enforcing security is a challenge due to the large number of devices forming the network and their limited computation capabilities. Furthermore, different security attacks require specifically tailored security protocols to prevent their occurrence. As an alternative to these conventional centralized security protocols, the application of Blockchain (BC) and Deep learning (DL) for securing IIoT networks hold great potential. BC facilitates security by being an immutable record of the changes happening in a network. Coalition Formation theory aids decentralization and promotes energy efficiency. And to enforce a state‐of‐the‐art attack detection technique, Deep learning provides an adaptive and reliable platform. Thus, in this paper, a security framework that facilitates generalized security for the IIoT network using BC and Coalition Formation theory is proposed. Additionally, we promote a sophisticated deep learning‐based classification algorithm to efficiently classify malicious and benign devices in IIoT scenarios. In the proposed model, connection links can only be established if the details of the connection are mined on the BC by the “sender” device. Therefore, we propose a Proof of Reliance algorithm that dynamically increases the computational difficulty to prevent malicious devices from attacking the network. Through simulations, it is experimentally proven that malicious devices can never attack the network when the proposed framework is employed for IIoT security.

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

A Survey of IIoT Protocols

Industrial Internet of Things (IIoT) is present in many participants from the energy, health, manufacturing, transport, and public sectors. Many factors catalyze IIoT, such as robotics, artificial intelligence, and intelligent decentralized manufacturing. However, the convergence between IT, OT, and IoT environments involves the integration of heterogeneous technologies through protocols, standards, and buses. However, this integration brings with it security risks. To avoid the security risks, especially when systems in different environments interact, it is important and urgent to create an early consensus among the stakeholders on the IIoT security. The default Common Vulnerability Scoring System (CVSS) offers a mechanism to measure the severity of an asset's vulnerability and therefore a way to characterize the risk. However, CVSS by default has two drawbacks. On the one hand, to carry out a risk analysis, it is necessary to have additional metrics to the one established by CVSSv3.1. On the other hand, this index has been used mostly in IT environments and although there are numerous efforts to develop a model that suits industrial environments, there is no established proposal. Therefore, we first propose a survey of the main 33 protocols, standards, and buses used in an IIoT environment. This survey will focus on the security of each one. The second part of our study consists of the creation of a framework to characterize risk in industrial environments, i.e., to solve both problems of the CVSS index. To this end, we created the Vulnerability Analysis Framework (VAF), which is a methodology that allows the analysis of 1,363 vulnerabilities to establish a measure to describe the risk in IIoT environments.

A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities

A key application of the Internet of Things (IoT) paradigm lies within industrial contexts. Indeed, the emerging Industrial Internet of Things (IIoT), commonly referred to as Industry 4.0, promises to revolutionize production and manufacturing through the use of large numbers of networked embedded sensing devices, and the combination of emerging computing technologies, such as Fog/Cloud Computing and Artificial Intelligence. The IIoT is characterized by an increased degree of inter-connectivity, which not only creates opportunities for the industries that adopt it, but also for cyber-criminals. Indeed, IoT security currently represents one of the major obstacles that prevent the widespread adoption of IIoT technology. Unsurprisingly, such concerns led to an exponential growth of published research over the last few years. To get an overview of the field, we deem it important to systematically survey the academic literature so far, and distill from it various security requirements as well as their popularity. This paper consists of two contributions: our primary contribution is a systematic review of the literature over the period 2011–2019 on IIoT Security, focusing in particular on the security requirements of the IIoT. Our secondary contribution is a reflection on how the relatively new paradigm of Fog computing can be leveraged to address these requirements, and thus improve the security of the IIoT.

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), malicious operation, malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security.

New Security Mechanisms of High-Reliability IoT Communication Based on Radio Frequency Fingerprint

Nowadays, the serious security threat of industrial control system and sensors has become a major challenge with the rapid development of Industrial Internet of Things (IIoT). Man-in-the-middle (MITM) attack is a very common intrusion method, which will make a great security threat in the application of IIoT. In IIoT scenario, the lightweight safety certification can play a very important role in the development of data-intensive and decentralized applications running on billions of sensors and devices, preserving their security. Therefore, in this paper, a low-latency high-reliability security mechanism is proposed to avoid the MITM attack in IIoT scenario. First, combining the radio frequency fingerprint (RFF) technology with IIoT applications, a lightweight IIoT security architecture is proposed. Based on the proposed IIoT security architecture, the process of device access authentication and communication service is illustrated. Second, according to the requirement of IIoT identification method, an access authentication method of the device is proposed based on the RFF. The method of feature extraction, classifier designing, and the access authentication process is discussed in detail. Finally, the simulation results show that the identification rate of devices can reach 95% under SNR = 6 dB, and can nearly reach 100% under SNR = 15 dB. Through the new process of access authentication, the access authentication rate can reach 95% under SNR = 15 dB. Therefore, according to the simulation results, the new security mechanisms based on RFF can be used to avoid the MITM attack in IIoT scenario.

A novel SMP-based survivability evaluation metric and approach in wireless sensor network

In Industrial Internet of Things (IIoT) device and network, wireless sensor network (WSN) is an important component. Routing protocol is the critical component of WSN. As the WSN may be attacked by all kinds of intruders, the survivability of WSN is important to IIoT security. To precisely evaluate the systematic survivability ability under external attack and internal security mechanism, a novel survivability entropy-based quantitative evaluation metric is proposed to calculate the systematic survivability ability of WSN routing protocol. Numerical analysis and simulation experiments are combined to precisely calculate the survivability entropy metric. To validate the evaluation approach, NS2 (Network simulator) is used to simulate the DoS attack and security mechanism in WSN. Experimental results show that the novel survivability evaluation metric and method can precisely evaluate the systematic survivability ability of WSN.

Toward Security Enhanced Provisioning in Industrial IoT Systems.

Through the active development of industrial internet of things (IIoT) technology, there has been a rapid increase in the number of different industrial wireless sensor networks (IWSNs). Accordingly, the security of IWSNs is also of importance, as many security problems related to IWSN protocols have been raised and various studies have been conducted to solve these problems. However, the provisioning process is the first step in introducing a new device into the IIoT network and a starting point for IIoT security. Therefore, leakage of security information in the provisioning process makes exposure of secret keys and all subsequent security measures meaningless. In addition, using the exploited secret keys, the attacker can send false command to the node or send false data to the network manager and it can cause serious damage to industrial infrastructure depending on the IWSN. Nevertheless, a security study on the provisioning process has not been actively carried out, resulting in a provisioning process without guaranteed security. Therefore, in this paper, we analyzed security issues of the provisioning process in IWSN by researching prominent IWSN standards, including ISA 100.11a, WirelessHART, and Zigbee, and also an ISA 100.11a-certified device and provisioning process-related studies. Then, we verified the security issues of the provisioning process through testing and analyzing the provisioning process using the ISA 100.11a standard-implemented devices and ISA 100.11a-certified devices. Finally, we discuss security considerations and the direction of future research on provisioning security for IWSN in the IIoT era.