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Special Issue "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: 31 August 2023 | Viewed by 5644

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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
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (7 papers)

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Research

Open AccessArticle

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

and

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

Viewed by 418

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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Open AccessArticle

Optimized and Efficient Image-Based IoT Malware Detection Method

and

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

Viewed by 730

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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Open AccessArticle

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

Huirong Zhu

Xuxu Zheng

and

Leina Zhao

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

Viewed by 742

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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Open AccessArticle

KryptosChain—A Blockchain-Inspired, AI-Combined, DNA-Encrypted Secure Information Exchange Scheme

Pratyusa Mukherjee

Chittaranjan Pradhan

Hrudaya Kumar Tripathy

and

Tarek Gaber

Electronics 2023, 12(3), 493; https://doi.org/10.3390/electronics12030493 - 17 Jan 2023

Viewed by 500

Abstract

Today’s digital world necessitates the adoption of encryption techniques to ensure secure peer-to-peer communication. The sole purpose of this paper is to conglomerate the fundamentals of Blockchain, AI (Artificial Intelligence) and DNA (Deoxyribonucleic Acid) encryption into one proposed scheme, KryptosChain, which is capable of providing a secure information exchange between a sender and his intended receiver. The scheme firstly suggests a DNA-based Huffman coding scheme, which alternatively allocates purines—Adenine (A) and Guanine (G), and pyrimidines—Thymine (T) and Cytosine (C) values, while following the complementary rule to higher and lower branches of the resultant Huffman tree. Inculcation of DNA concepts makes the Huffman coding scheme eight times stronger than the traditional counterpart based on binary—0 and 1 values. After the ciphertext is obtained, the proposed methodology next provides a Blockchain-inspired message exchange scheme that achieves all the principles of security and proves to be immune to common cryptographic attacks even without the deployment of any smart contract, or possessing any cryptocurrency or arriving at any consensus. Lastly, different classifiers were engaged to check the intrusion detection capability of KryptosChain on the NSL-KDD dataset and AI fundamentals. The detailed analysis of the proposed KryptosChain validates its capacity to fulfill its security goals and stands immune to cryptographic attacks. The intrusion possibility curbing concludes that the J84 classifier provides the highest accuracy of 95.84% among several others as discussed in the paper. Full article

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

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Open AccessArticle

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

Nejood Faisal Abdulsattar

Dheyaa Abdulameer Mohammed

Mohammed Ahmed Jubair

and

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

Viewed by 661

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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Open AccessArticle

Improving Healthcare Applications Security Using Blockchain

Ibrahim Shawky Farahat

and

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

Cited by 1 | Viewed by 706

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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Open AccessArticle

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

Chi Trung Ngo

Jason K. Eshraghian

and

Jong-Phil Hong

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

Viewed by 690

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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