Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.     

A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment

As communication technology and smart manufacturing have developed, the industrial internet of things (IIoT) has gained considerable attention from academia and industry. Wireless sensor networks (WSNs) have many advantages with broad applications in many areas including environmental monitoring, which makes it a very important part of IIoT. However, energy depletion and hardware malfunctions can lead to node failures in WSNs. The industrial environment can also impact the wireless channel transmission, leading to network reliability problems, even with tightly coupled control and data planes in traditional networks, which obviously also enhances network management cost and complexity. In this paper, we introduce a new software defined network (SDN), and modify this network to propose a framework called the improved software defined wireless sensor network (improved SD-WSN). This proposed framework can address the following issues. 1) For a large scale heterogeneous network, it solves the problem of network management and smooth merging of a WSN into IIoT. 2) The network coverage problem is solved which improves the network reliability. 3) The framework addresses node failure due to various problems, particularly related to energy consumption. Therefore, it is necessary to improve the reliability of wireless sensor networks, by developing certain schemes to reduce energy consumption and the delay time of network nodes under IIoT conditions. Experiments have shown that the improved approach significantly reduces the energy consumption of nodes and the delay time, thus improving the reliability of WSN.      

Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

In recent years, Digital Twin (DT) has gained significant interest from academia and industry due to the advanced in information technology, communication systems, Artificial Intelligence (AI), Cloud Computing (CC), and Industrial Internet of Things (IIoT). The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complexity during the life cycle that produces a massive amount of engendered data and information. Likewise, with the development of AI, digital twins can be redefined and could be a crucial approach to aid the Internet of Things (IoT)-based DT applications for transferring the data and value onto the Internet with better decision-making. Therefore, this paper introduces an efficient DT-based fault diagnosis model based on machine learning (ML) tools. In this framework, the DT model of the machine is constructed by creating the simulation model. In the proposed framework, the Genetic algorithm (GA) is used for the optimization task to improve the classification accuracy. Furthermore, we evaluate the proposed fault diagnosis framework using performance metrics such as precision, accuracy, F-measure, and recall. The proposed framework is comprehensively examined using the triplex pump fault diagnosis. The experimental results demonstrated that the hybrid GA-ML method gives outstanding results compared to ML methods like Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machine (SVM). The suggested framework achieves the highest accuracy of 95% for the employed hybrid GA-SVM. The proposed framework will effectively help industrial operators make an appropriate decision concerning the fault analysis for IIoT applications in the context of Industry 4.0.     

Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

The rapid growth of the Internet of Things (IoT) in the industrial sector has given rise to a new term: the Industrial Internet of Things (IIoT). The IIoT is a collection of devices, apps, and services that connect physical and virtual worlds to create smart, cost-effective, and scalable systems. Although the IIoT has been implemented and incorporated into a wide range of industrial control systems, maintaining its security and privacy remains a significant concern. In the IIoT contexts, an intrusion detection system (IDS) can be an effective security solution for ensuring data confidentiality, integrity, and availability. In this paper, we propose an intelligent intrusion detection technique that uses principal components analysis (PCA) as a feature engineering method to choose the most significant features, minimize data dimensionality, and enhance detection performance. In the classification phase, we use clustering algorithms such as K-medoids and K-means to determine whether a given flow of IIoT traffic is normal or attack for binary classification and identify the group of cyberattacks according to its specific type for multi-class classification. To validate the effectiveness and robustness of our proposed model, we validate the detection method on a new driven IIoT dataset called X-IIoTID. The performance results showed our proposed detection model obtained a higher accuracy rate of 99.79% and reduced error rate of 0.21% when compared to existing techniques.     

工业物联网数据流自适应聚类方法

5G通讯技术的迅猛发展使工业物联网得到了全面提升, 工业物联网数据规模将越来越大、数据维度也越来越高, 如何高效利用流聚类进行工业物联网数据挖掘工作是一个亟需解决的问题. 提出了一种基于工业物联网数据流自适应聚类方法. 该算法利用微簇之间的高密性, 计算各微簇节点的局部密度峰值以自适应产生宏簇数; 采用引力能量函数对微集群进行递归在线更新; 并且去除边缘相交微簇之间的计算以达到降低维护宏簇所需的计算量. 理论分析和实验对比表明所提出的方法跟当前主流的流聚类算法相比有着更高质量的聚类效果.     

面向工业物联网环境下后门隐私泄露感知方法

伴随工业物联网相关技术的高速发展,后门隐私信息的泄露正成为一个重大的挑战,严重威胁工业控制系统及物联网环境的安全性及稳定性.本文基于工业物联网环境下后门隐私的数据特征定义若干基本属性,根据静态及动态数据流安全威胁抽取上层语义,并基于多属性决策方法聚合生成静态与动态泄露度,最终结合灰色关联分析计算安全级与安全阈值,以此实现后门隐私信息在静态二进制结构及动态数据流向中的泄露场景感知.实验选择目标环境中27种后门隐私信息进行测试,依次计算并分析基本定义、上层语义及判决语义,通过安全级与安全阈值的比较成功感知多种后门泄露场景.实验还将本文工作与其他相关模型或系统进行对比,验证了所提方法的有效性.     

Securing IIoT using Defence-in-Depth: Towards an End-to-End secure Industry 4.0

Industry 4.0 uses a subset of the IoT, called Industrial IoT (IIoT) to achieve connectivity, interoperability and decentralisation. The deployment of industrial networks rarely considers security by design, but this becomes imperative in smart manufacturing as connectivity increases. The combination of OT and IT infrastructures in Industry 4.0 adds new security threats beyond those of traditional industrial networks. Defence-in-Depth (DiD) strategies tackle the complexity of this problem by providing multiple defence layers, each of these focusing on a particular set of threats. Additionally, the severe requirements of IIoT networks demand lightweight encryption algorithms. Nevertheless, these ciphers must provide E2E (End-to-End) security, as data pass through intermediate entities, or middleboxes, before reaching its destination. If compromised, middleboxes could expose vulnerable information to potential attackers if it is not encrypted throughout this path. This paper presents an analysis of the most relevant security strategies in Industry 4.0, focusing primarily on DiD. With these in mind, it proposes a combination of DiD, a lightweight E2E encryption algorithm called Attribute-Based-Encryption (ABE) and object security (i.e., OSCORE) to get a full E2E security approach. This analysis is a critical first step to develop more complex and lightweight security frameworks suitable for Industry 4.0.     

Application of the Internet of Things in the textile industry

Abstract

The ‘Internet of Things’ (IoT) is at times mythologized and its purpose mistaken, and often people can become confused about what it means, does or aims to achieve; moreover, without a financial appraisal of the differences IoT can make to their own enterprise, business leaders may be content to watch and wait rather than to take a lead. This issue of Textile Progress aims to provide a guide to help navigation beyond general statements about IoT and to help those involved with the textile industry to make an informed decision about its potential value to them. The definitions of the Internet of Things (IoT) available in the literature, and those put forth by organizations working on IoT standards development, are reviewed along with its architecture, elements, standards and protocols to help provide an understanding of the concepts and goals of IoT. A broad overview of impediments challenging the progress of IoT, especially in relation to cybersecurity, is provided. This review also compares ongoing work in the application of IoT in the textile industry to that in other manufacturing sectors whilst focussing on the interpretation of IoT technologies and their potential application to the textile industry according to both technological and business perspectives. A specific case study for the spinning industry is conducted to help with evaluation of its IoT solutions and to help to address how other parts of the textile industry might benefit from its application. The case study includes evaluation of IoT solutions in the spinning industry spanning the period between ITMA 2011 to 2019, and takes information from interviews with industry executives to inform future directions of IoT in the spinning industry.     

Wrapper Based Linear Discriminant Analysis (LDA) for Intrusion Detection in IIoT

The internet has become a part of every human life. Also, various devices that are connected through the internet are increasing. Nowadays, the Industrial Internet of things (IIoT) is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development. Moreover, IIoT is being used in various industrial fields such as logistics, manufacturing, metals and mining, gas and oil, transportation, aviation, and energy utilities. It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks. Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive data. Intrusion Detection Systems (IDS) have taken an important role in providing security in the field of computer networks. Prevention of intrusion is completely based on the detection functions of the IDS. When an IIoT network expands, it generates a huge volume of data that needs an IDS to detect intrusions and prevent network attacks. Many research works have been done for preventing network attacks. Every day, the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly defined. In this regard, this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA (WDLDA). The implemented WDLDA results in a rate of detection accuracy (DRA) of 97% and a false positive rate (FPR) of 11% using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset.     

A way forward towards a technology-driven development of industry 4.0 using big data analytics in 5G-enabled IIoT

The evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G-enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G-enabled IIoT. The paper presents the state-of-the-art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT-driven 5G technology. We have also highlighted the revolution of IIoT-driven 5G framework which satisfies the demands of IIoT applications.