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Artificial Intelligence in Cybersecurity for Industry 4.0

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 27535

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Special Issue Editors

Dr. Tarek Gaber

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Guest Editor

School of Science, Engineering & Environment, University of Salford, Greater Manchester M5 4WT, UK
Interests: biometric authentication and identification; cybersecurity; machine learning; secure software engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Joseph Bamidele Awotunde

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Guest Editor

Department of Computer Science, University of Ilorin, Ilorin, Nigeria
Interests: Internet of Things; information security; social computing; bioinformatics; artificial intelligence

Dr. Ali Ahmed

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Guest Editor

School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
Interests: distance learning; engineering education; software developer's education tools; IoT in education

Special Issue Information

Dear Colleagues,

For all firms committed to the Industry 4.0 concept, cybersecurity risks pose critical challenges. On the other hand, the categorization of cybersecurity concepts within Industry 4.0 contexts has emerged as an emergent and essential topic in recent research. Studies on security and privacy for Industry 4.0 with a multi-cybersecurity formation have been of interest for the last decade, slowly moving towards intelligent industry techniques buttressed by advances in communication, sensing techniques, and the Industrial Internet of Things (IIoT). Industry 4.0 requires strong cybersecurity schemes that balance the desired communication and mechanism effect with the computational result. However, many proposed research methods and techniques are not yet suitable for use in cybersecurity for Industry 4.0.

In recent years, artificial intelligence (AI) has promptly gained aggregate interest in cybersecurity for Industry 4.0, having demonstrated its tremendous usefulness in a wide variety of applications. AI methods pave new ways for the state-of-the-art in multiscale security and privacy in cybersecurity for Industry 4.0, such as AI-based intrusion detection prediction and classification, AI-based malicious and intruder protection, etc. Likewise, AI-based schemes also have noteworthy potential to overcome many of the cybersecurity challenges facing Industry 4.0, which is also known as IIoT.

To motivate the creation of innovative AI-based applications in cybersecurity for Industry 4.0 towards security driving, the goal of this Special Issue is to find exceptional solutions among the high-quality submissions, providing key insights into cybersecurity and the implications for achieving the Industry 4.0 goal, exploring associated technologies such as the Industrial Internet of Things and cloud-based design and manufacturing systems, and including a detailed examination of the internet's economies of scale and related cybersecurity problems.

The suggested topics include, but are not limited to:

AI-based security strategies for cybersecurity in Industry 4.0;
AI-based multiscale cybersecurity management in Industry 4.0, e.g., security management problems in IIoT systems;
AI-based security intelligence for Healthcare Industry 4.0;
AI-based paradigm for cloud-based smart factory of Industry 4.0;
AI-based intrusion detection of Industry 4.0;
AI-based securing devices, sensors, and wearable systems of IIoT systems;
Security and privacy of Industry 4.0;
Machine learning for cybersecurity of Industry 4.0;
Deep learning for cybersecurity of Industry 4.0.

Dr. Tarek Gaber
Dr. Joseph Bamidele Awotunde
Dr. Ali Ahmed
Guest Editors

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Keywords

Artificial Intelligence
Machine learning
Deep learning
Cybersecurity
Privacy
Industrial Internet of Things
Industry 4.0

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Published Papers (8 papers)

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Research

17 pages, 5904 KiB

Open AccessArticle

Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems

by Louai A. Maghrabi, Ibrahim R. Alzahrani, Dheyaaldin Alsalman, Zenah Mahmoud AlKubaisy, Diaa Hamed and Mahmoud Ragab

Electronics 2023, 12(19), 4091; https://doi.org/10.3390/electronics12194091 - 29 Sep 2023

Cited by 6 | Viewed by 1867

Abstract

Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Golden Jackal Optimization with Deep Learning-based Cyberattack Detection and Classification (GJODL-CADC) method in the IIoT platform. The major objective of the GJODL-CADC system lies in the detection and classification of cyberattacks on the IoT platform. To obtain this, the GJODL-CADC algorithm presents a new GJO-based feature selection approach to improve classification accuracy. Next, the GJODL-CADC method makes use of a hybrid autoencoder-based deep belief network (AE-DBN) approach for cyberattack detection. The effectiveness of the AE-DBN approach can be improved through the design of the pelican optimization algorithm (POA), which in turn improves the detection rate. An extensive set of simulations were accomplished to demonstrate the superior outcomes of the GJODL-CADC technique. An extensive analysis highlighted the promising performance of the GJODL-CADC technique compared to existing techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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26 pages, 10737 KiB

Open AccessArticle

Agentless Approach for Security Information and Event Management in Industrial IoT

by Huma Zahid, Sadaf Hina, Muhammad Faisal Hayat and Ghalib A. Shah

Electronics 2023, 12(8), 1831; https://doi.org/10.3390/electronics12081831 - 12 Apr 2023

Cited by 13 | Viewed by 4641

Abstract

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. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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18 pages, 1480 KiB

Open AccessArticle

Intrusion Detection Method Based on CNN–GRU–FL in a Smart Grid Environment

by Feng Zhai, Ting Yang, Hao Chen, Baoling He and Shuangquan Li

Electronics 2023, 12(5), 1164; https://doi.org/10.3390/electronics12051164 - 28 Feb 2023

Cited by 19 | Viewed by 2889

Abstract

The aim of this paper is to address the current situation where business units in smart grid (SG) environments are decentralized and independent, and there is a conflict between the need for data privacy protection and network security monitoring. To address this issue, we propose a distributed intrusion detection method based on convolutional neural networks–gated recurrent units–federated learning (CNN–GRU–FL). We designed an intrusion detection model and a local training process based on convolutional neural networks–gated recurrent units (CNN–GRU) and enhanced the feature description ability by introducing an attention mechanism. We also propose a new parameter aggregation mechanism to improve the model quality when dealing with differences in data quality and volume. Additionally, a trust-based node selection mechanism was designed to improve the convergence ability of federated learning (FL). Through experiments, it was demonstrated that the proposed method can effectively build a global intrusion detection model among multiple independent entities, and the training accuracy rate, recall rate, and F1 value of CNN–GRU–FL reached 78.79%, 64.15%, and 76.90%, respectively. The improved mechanism improves the performance and efficiency of parameter aggregation when there are differences in data quality. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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21 pages, 2159 KiB

Open AccessArticle

Optimized and Efficient Image-Based IoT Malware Detection Method

by Amir El-Ghamry, Tarek Gaber, Kamel K. Mohammed and Aboul Ella Hassanien

Electronics 2023, 12(3), 708; https://doi.org/10.3390/electronics12030708 - 31 Jan 2023

Cited by 28 | Viewed by 3619

Abstract

With the widespread use of IoT applications, malware has become a difficult and sophisticated threat. Without robust security measures, a massive volume of confidential and classified data could be exposed to vulnerabilities through which hackers could do various illicit acts. As a result, improved network security mechanisms that can analyse network traffic and detect malicious traffic in real-time are required. In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (i.e., images) of the network traffic. In this method, the ant colony optimizer (ACO)-based feature selection method was proposed to get a minimum number of features while improving the support vector machines (SVMs) classifier’s results (i.e., the malware detection results). Further, the PSO algorithm tuned the SVM parameters of the different kernel functions. Using a public dataset, the experimental results showed that the SVM linear function kernel is the best with an accuracy of 95.56%, recall of 96.43%, precision of 94.12%, and F1_score of 95.26%. Comparing with the literature, it was concluded that bio-inspired techniques, i.e., ACO and PSO, could be used to build an effective and lightweight machine-learning-based malware detection system for the IoT environment. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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13 pages, 1520 KiB

Open AccessArticle

Analysis of Teachers’ Cognitive Ability and Teaching Motivation on the Academic Achievement of Students in Higher Education via Employment Data Guidance

by Huirong Zhu, Xuxu Zheng and Leina Zhao

Electronics 2023, 12(3), 572; https://doi.org/10.3390/electronics12030572 - 23 Jan 2023

Cited by 3 | Viewed by 2493

Abstract

Teachers and students are the two basic elements in educational activities. Students are educated but are not exactly passive recipients of education. With subjective initiative, all educational impacts must be through the initiative of students to achieve the desired effect. Therefore, all activities of education must start from mobilizing students’ initiative and motivation so that they have sufficient motivation to learn actively and well. The effective analysis of employment data, at the statistical level of data analysis, is a favorable basis to support the influence of teachers on students. However, most of the previous methods are C4.5 algorithms, decision tree generation algorithms based on rough sets, etc., which are commonly used for employment data analysis. None of them can sufficiently deal with the problem of different decision accuracy requirements and noise adaptability. In this paper, we analyze the employment data of a university in 2012 as an example and compare the analysis results with those of the C4.5 algorithm and decision tree generation algorithm based on a rough set. The results show that the decision tree algorithm based on the multiscale rough set model generates a simple decision tree structure. In addition, our methods do not have indistinguishable datasets and are fast in terms of computing. This study provides an effective guide to the relevance of teachers’ cognitive abilities and teaching motivations for students’ employment. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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29 pages, 6725 KiB

Open AccessArticle

Privacy-Preserving Mobility Model and Optimization-Based Advanced Cluster Head Selection (P2O-ACH) for Vehicular Ad Hoc Networks

by Nejood Faisal Abdulsattar, Dheyaa Abdulameer Mohammed, Ahmed Alkhayyat, Shemaha Z. Hamed, Hussein Muhi Hariz, Ali S. Abosinnee, Ali Hashim Abbas, Mustafa Hamid Hassan, Mohammed Ahmed Jubair, Fatima Hashim Abbas, Abeer D. Algarni, Naglaa F. Soliman and Walid El-Shafai

Electronics 2022, 11(24), 4163; https://doi.org/10.3390/electronics11244163 - 13 Dec 2022

Cited by 44 | Viewed by 1976

Abstract

In vehicular ad hoc networks (VANETs), due to the fast-moving mobile nodes, the topology changes frequently. This dynamically changing topology produces congestion and instability. To overcome this issue, privacy-preserving optimization-based cluster head selection (P2O-ACH) is proposed. One of the major drawbacks analyzed in the earlier cluster-based VANETs is that it creates a maximum number of clusters for communication that leads to an increase in energy consumption which reflects in a degradation of the performance. In this paper, enhanced rider optimization algorithm (ROA)-based CH selection is performed and that optimally selects the CH so that effective clusters are created. By analyzing this, the behavior of the bypass rider’s CH is chosen, and this forms the optimized clusters, and during the process of transmission, privacy-preserving mobility patterns are used to secure the network from all kinds of malfunctions which are performed by the new vehicle blending and migration process. The proposed P2O-ACH is simulated using NS-2, and for performance analysis, two scenarios are taken, which contain a varying number of vehicles and varying speeds. For a varying number of vehicles and speeds, the considered parameters are energy efficiency, energy consumption, network lifetime, packet delivery ratio, packet loss, network latency, network throughput, and routing overhead. From the results, it is understood that the proposed method performed better when compared with earlier work, such as GWO-CH, ACO-SCRS, and QMM-VANET. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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16 pages, 3042 KiB

Open AccessArticle

Improving Healthcare Applications Security Using Blockchain

by Ibrahim Shawky Farahat, Waleed Aladrousy, Mohamed Elhoseny, Samir Elmougy and Ahmed Elsaid Tolba

Electronics 2022, 11(22), 3786; https://doi.org/10.3390/electronics11223786 - 17 Nov 2022

Cited by 7 | Viewed by 4835

Abstract

Nowadays, the Internet of Medical Things (IoMT) technology is growing and leading the revolution in the global healthcare field. Exchanged information through IoMT permits attackers to hack or modify the patient’s data. Hence, it is of critical importance to ensure the security and privacy of this information. The standard privacy techniques are not secured enough, so this paper introduces blockchain technology that is used for securing data. Blockchain is used with the smart contract to secure private patient records. This paper presents how a patient may send his vital signs to the physician through the Internet without meeting with the latter in person. These vital signs are collected from the IoMT system that we developed before. In the proposed method, each medical record is stored in the block and connected to the previous block by a hashing function. In order to secure the new block, the SHA256 algorithm is used. We modified the SHA256 algorithm by using run-length code in compressing data. If any hacker attempts to attack any medical record, he must change all previous blocks. In order to preserve the rights of the doctor and patient, a smart contract is built into the blockchain system. When the transaction begins, the smart contract withdraws the money from the patient’s wallet and stores it in the smart contract. When the physician sends the treatment to the patient, the smart contract transfers the money to the physician. This paper shows that all recent work implements Blockchain 2 into the security system. This paper also shows that our security system can create a new block with O (n + d) time complexity. As a result, our system can create one hundred blocks in two minutes. Additionally, our system can deposit the money from the patient’s wallet into the physician’s wallet promptly. This paper also shows that our method performs better than all subsequent versions of the original blockchain. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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16 pages, 23602 KiB

Open AccessArticle

An Area-Optimized and Power-Efficient CBC-PRESENT and HMAC-PHOTON

by Chi Trung Ngo, Jason K. Eshraghian and Jong-Phil Hong

Electronics 2022, 11(15), 2380; https://doi.org/10.3390/electronics11152380 - 29 Jul 2022

Cited by 3 | Viewed by 2293

Abstract

This paper introduces an area-optimized and power-efficient implementation of the Cipher Block Chaining (CBC) mode for an ultra-lightweight block cipher, PRESENT, and the Keyed-Hash Message Authentication Code (HMAC)-expanded PHOTON by using a feedback path for a single block in the scheme. The proposed scheme is designed, taped out, and integrated as a System-on-a-Chip (SoC) in a 65-nm CMOS process. An experimental analysis and comparison between a conventional implementation of CBC-PRESENT/HMAC-PHOTON with the proposed feedback basis is performed. The proposed CBC-PRESENT/HMAC-PHOTON has 128-bit plaintext/text and a 128-bit secret key, which have a gate count of 5683/20,698 and low power consumption of 1.03/2.62 mW with a throughput of 182.9/14.9 Mbps at the maximum clock frequency of 100 MHz, respectively. The overall improvement in area and power dissipation is 13/50.34% and 14.87/75.28% when compared to a conventional design. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity for Industry 4.0)

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