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AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (1 February 2024) | Viewed by 30281

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Dr. Piyush Kumar Shukla

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Department of Computer Science, University Institute of Technology, RGPV Bhopal, Bhopal 462033, India
Interests: white box cryptography; information security; privacy; cyber security; dynamic wireless networks; blockchain; machine learning; IoT; image processing; medical imaging; FANET
Special Issues, Collections and Topics in MDPI journals

Dr. Ahmed A. Abd El-Latif

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Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt
Interests: quantum information processing; information security; cybersecurity; information hiding; biometrics; internet of things (IoT); big data; blockchain; forensic analysis in digital images
Special Issues, Collections and Topics in MDPI journals

Dr. Rupak Kharel

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Guest Editor

Faculty of Science and Technology, University of Central Lancashire, Preston PR1 2HE, UK
Interests: cyber-security; physical layer security; Internet of Things (IoT); Internet of Vehicles (IoV)
Special Issues, Collections and Topics in MDPI journals

Dr. Udai Pratap Rao

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Guest Editor

Computer Science and Engineering Department, S.V. National Institute of Technology, Gujarat 395007, India
Interests: information security & privacy; privacy in location-based service; cyber-physical systems (CPSs); IoT

Dr. Prashant Kumar Shukla

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Guest Editor

Department of Computer Science & Engineering, K L University, Vijaywada, Andhra Pradesh, India
Interests: machine learning; IoT; CPS; blockchain; wireless network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and Blockchain technology have a wide range of interdisciplinary convergence. In recent years, advances in artificial intelligence and Blockchain technology have demonstrated a substantial increase in Cyber–Physical Systems’ (CPSs) attacks on defences. However, CPSs encourage new technological reforms in Industry 4.0, but still, various security challenges need to be addressed. Recently AI has been used for securing the CPS ecosystem with various innovative techniques and unique futuristic ideas. We are, however, also witnessing an increased use of AI, deep learning and ML based techniques via adversaries to perform attacks on IoT systems, in addition to the traditional methods where sensor data were captured and manipulated. This has increased the attack surface in such systems and introduced an ever evolving threat landscape.

Blockchain technology is widely used to design a decentralized and transparent distributed system. Blockchain works as a distributed digital ledger of cryptographically signed transactions grouped into blocks where crucial IoT data are kept, while maintaining integrity. Recently, its footprint can be observed in the protection of Modern Cyber–Physical Systems applications; the main attributes of Blockchain technology are, e.g., transparency, decentralization, reliable database, collective maintenance, traceability, security and credibility. Applications of AI and Blockchain (AI-BC) technology with Cyber–Physical Systems (CPSs) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a quite challenging task because of the complexity associated with them.

SCs are modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration and service costs. It also improves system efficiency and reduces the associated security risks. However, while SCs encourage new technological reforms in Industry 4.0, various security and privacy challenges still need to be addressed.

In this Special Issue proposal, various open issues and challenges for AI-based SPCSs have been identified, and Survey/Review papers, Case Studies Technical papers based on Artificial Intelligence (AI) assisted techniques and tools for Securing SPCSs will be invited.

The proposed Special Issue aims to promote research and reflect the most recent advances focused on applying advanced Security techniques and Blockchain technology combined with Artificial Intelligence based Deep Learning Techniques for Modern Cyber–Physical Systems.

Topics of interest include but are not limited to:

Security Techniques for CPSs Assisted Blockchain and and IoT;
Cryptographic Techniques for CPSs
AI, ML and DL Approaches for CPSs Blockchain and IoT;
AI-Assisted CPSs Systems
Modelling of Intelligent Attacks, Threats using ML/DL for CPSs
Penetration testing using ML/DL on CPSs based Blockchain and IoT
Deep/Machine Learning for Securing CPSs
Reinforcement learning for Securing CPSs
Access Control, Authentication and Cryptographic analysis with ML/DL for CPSs
Trojan Horse and phishing analysis and detection using ML/
Big Data and Data Science-based Security Solutions for CPSs
IoT and Blockchain-based security solutions for CPSs Modern Smart Applications;
Intelligent and Dynamic Attack Moderation for CPSs
Intrusion detection for CPSs;
Wireless sensor network security for Blockchain and and IoT enabled CPSs
IoT and security hardware designs and implementations;
Safety, governance, risk management, compliance, and trust management in IoT;
Estimation theory for secure communication protocols for CPSs

Dr. Piyush Kumar Shukla
Dr. Ahmed A. Abd El-Latif
Dr. Rupak Kharel
Dr. Udai Pratap Rao
Dr. Prashant Kumar Shukla
Guest Editors

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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.

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Published Papers (9 papers)

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Research

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27 pages, 2738 KiB

Open AccessArticle

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

by Eman Abdullah Aldakheel, Mohammed Zakariah, Ghada Abdalaziz Gashgari, Fahdah A. Almarshad and Abdullah I. A. Alzahrani

Sensors 2023, 23(9), 4403; https://doi.org/10.3390/s23094403 - 30 Apr 2023

Cited by 11 | Viewed by 4544

Abstract

Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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20 pages, 3223 KiB

Open AccessArticle

A Novel Cipher-Based Data Encryption with Galois Field Theory

by Mohammad Mazyad Hazzazi, Sasidhar Attuluri, Zaid Bassfar and Kireet Joshi

Sensors 2023, 23(6), 3287; https://doi.org/10.3390/s23063287 - 20 Mar 2023

Cited by 7 | Viewed by 1780

Abstract

Both the act of keeping information secret and the research on how to achieve it are included in the broad category of cryptography. When people refer to “information security,” they are referring to the study and use of methods that make data transfers harder to intercept. When we talk about “information security,” this is what we have in mind. Using private keys to encrypt and decode messages is a part of this procedure. Because of its vital role in modern information theory, computer security, and engineering, cryptography is now considered to be a branch of both mathematics and computer science. Because of its mathematical properties, the Galois field may be used to encrypt and decode information, making it relevant to the subject of cryptography. The ability to encrypt and decode information is one such use. In this case, the data may be encoded as a Galois vector, and the scrambling process could include the application of mathematical operations that involve an inverse. While this method is unsafe when used on its own, it forms the foundation for secure symmetric algorithms like AES and DES when combined with other bit shuffling methods. A two-by-two encryption matrix is used to protect the two data streams, each of which contains 25 bits of binary information which is included in the proposed work. Each cell in the matrix represents an irreducible polynomial of degree 6. Fine-tuning the values of the bits that make up each of the two 25-bit binary data streams using the Discrete Cosine Transform (DCT) with the Advanced Encryption Standard (AES) Method yields two polynomials of degree 6. Optimization is carried out using the Black Widow Optimization technique is used to tune the key generation in the cryptographic processing. By doing so, we can produce two polynomials of the same degree, which was our original aim. Users may also use cryptography to look for signs of tampering, such as whether a hacker obtained unauthorized access to a patient’s medical records and made any changes to them. Cryptography also allows people to look for signs of tampering with data. Indeed, this is another use of cryptography. It also has the added value of allowing users to check for indications of data manipulation. Users may also positively identify faraway people and objects, which is especially useful for verifying a document’s authenticity since it lessens the possibility that it was fabricated. The proposed work achieves higher accuracy of 97.24%, higher throughput of 93.47%, and a minimum decryption time of 0.0047 s. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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17 pages, 911 KiB

Open AccessArticle

BlockEdge: A Privacy-Aware Secured Edge Computing Framework Using Blockchain for Industry 4.0

by Deepsubhra Guha Roy

Sensors 2023, 23(5), 2502; https://doi.org/10.3390/s23052502 - 23 Feb 2023

Cited by 2 | Viewed by 1558

Abstract

Edge computing has its application in a lot of areas now, but with the increasing popularity and benefits, it suffers from some challenges such as data privacy and security. Intruder attacks should be prevented and only authentic users should have access to data storage. Most of the authentication techniques apply some trusted entity to undergo the process. Users and servers both have to be registered in the trusted entity to get permission of authenticating other users. In this scenario, the entire system depends on a single trusted entity; so, a single point of failure can cause the failure of the total system, and scalability issues are there also. To address these issues remaining in the existing systems, in this paper, a decentralized approach has been discussed which is capable of eliminating the concept of a single trusted entity by introducing a blockchain paradigm in edge computing where every time a user or server wants to enter the system, it does not have to register itself manually, but the authentication process is carried out throughout the scheme automatically. Experimental results and performance analysis prove that the proposed architecture is definitely beneficial and it outperforms the existing ones in the concerned domain. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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19 pages, 2502 KiB

Open AccessArticle

Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map

by Majed Alsafyani, Fahad Alhomayani, Hatim Alsuwat and Emad Alsuwat

Sensors 2023, 23(3), 1415; https://doi.org/10.3390/s23031415 - 27 Jan 2023

Cited by 6 | Viewed by 2224

Abstract

Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study’s findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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19 pages, 2911 KiB

Open AccessArticle

A Research on the Sharing Platform of Wild Bird Data in Yunnan Province Based on Blockchain and Interstellar File System

by Huaiyuan Yang, Yucheng Li, Hua Zhou, Yili Zhao and Lei Song

Sensors 2022, 22(18), 6961; https://doi.org/10.3390/s22186961 - 14 Sep 2022

Cited by 1 | Viewed by 1219

Abstract

Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain’s decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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18 pages, 4315 KiB

Open AccessArticle

A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks

by Kranthi Kumar Singamaneni, Gaurav Dhiman, Sapna Juneja, Ghulam Muhammad, Salman A. AlQahtani and John Zaki

Sensors 2022, 22(18), 6741; https://doi.org/10.3390/s22186741 - 06 Sep 2022

Cited by 28 | Viewed by 1728

Abstract

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber–physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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11 pages, 482 KiB

Open AccessArticle

Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning

by Apeksha Aggarwal, Akshat Srivastava, Ajay Agarwal, Nidhi Chahal, Dilbag Singh, Abeer Ali Alnuaim, Aseel Alhadlaq and Heung-No Lee

Sensors 2022, 22(6), 2378; https://doi.org/10.3390/s22062378 - 19 Mar 2022

Cited by 45 | Viewed by 7244

Abstract

Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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Review

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25 pages, 2628 KiB

Open AccessReview

High-Speed Network DDoS Attack Detection: A Survey

by Rana M. Abdul Haseeb-ur-rehman, Azana Hafizah Mohd Aman, Mohammad Kamrul Hasan, Khairul Akram Zainol Ariffin, Abdallah Namoun, Ali Tufail and Ki-Hyung Kim

Sensors 2023, 23(15), 6850; https://doi.org/10.3390/s23156850 - 01 Aug 2023

Cited by 3 | Viewed by 3554

Abstract

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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20 pages, 460 KiB

Open AccessReview

Cross Channel Scripting and Code Injection Attacks on Web and Cloud-Based Applications: A Comprehensive Review

by Indushree M, Manjit Kaur, Manish Raj, Shashidhara R and Heung-No Lee

Sensors 2022, 22(5), 1959; https://doi.org/10.3390/s22051959 - 02 Mar 2022

Cited by 8 | Viewed by 4333

Abstract

Cross channel scripting (XCS) is a common web application vulnerability, which is a variant of a cross-site scripting (XSS) attack. An XCS attack vector can be injected through network protocol and smart devices that have web interfaces such as routers, photo frames, and cameras. In this attack scenario, the network devices allow the web administrator to carry out various functions related to accessing the web content from the server. After the injection of malicious code into web interfaces, XCS attack vectors can be exploited in the client browser. In addition, scripted content can be injected into the networked devices through various protocols, such as network file system, file transfer protocol (FTP), and simple mail transfer protocol. In this paper, various computational techniques deployed at the client and server sides for XCS detection and mitigation are analyzed. Various web application scanners have been discussed along with specific features. Various computational tools and approaches with their respective characteristics are also discussed. Finally, shortcomings and future directions related to the existing computational techniques for XCS are presented. Full article

(This article belongs to the Special Issue AI & Blockchain Assisted Innovative Techniques & Solutions to Modern CPS Security for Industry 4.0/5.0)

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