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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: 15 January 2025 | Viewed by 14642

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Special Issue Editors

Prof. Dr. Mikolaj Karpinski

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Department of Computer Science and Automatics, University of Bielsko-Biala, 43-300 Bielsko-Biala, Poland
Interests: computer networks and wireless communication; computer security and cryptography; computing systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Oleksandr O. Kuznetsov

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1. Department of information systems and technologies security, V.N. Karazin Kharkiv National University, 61022 Kharkiv, Ukraine
2. Department of Political Sciences, Communication and International Relations, University of Macerata, Via Crescimbeni, 62100 Macerata, Italy
Interests: noise-tolerant transmission of information; algebraic code theory; authentication theory; cryptography and steganography
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Roman Oliynykov

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Guest Editor

1. Input Output (IOG Singapore Pte Ltd), 4 Battery Road, Singapore
2. School of Computer Science, V. N. Karazin Kharkiv National University, 61022 Kharkiv, Ukraine
Interests: blockchain; cryptography ; cybersecurity

Special Issue Information

Dear Colleagues,

The introduction of modern computer technologies, intelligent management methods, advanced robotics, artificial intelligence (AI), analysis, control and diagnostic systems increases the efficiency of production processes, accelerates exchange, reduces costs and improves the use of resources in a variety of areas: the financial and banking sector, high-tech manufacturing, the military-industrial sector, energy, transport, health care, environment, the penitentiary system, entertainment industry, etc. However, it also has negative aspects: new risks and threats of disclosure of confidential information, compromise, distortion or damage to hardware and software and information resources, disruption or improper provision of services, etc. In particular, there are already many issues of malicious intrusions into information systems, including critical infrastructure systems, where failure, malfunction or exceedance of certain parameters leads to disastrous consequences in energy, transport, banking, military, environment, etc.

Thus, the main challenge of the modern digital world is the problem of computer security, which consists of protecting all components of the information infrastructure: computing devices and supporting equipment, computer networks and their components and protocols and algorithms for information storage, exchange, processing and transmission. To solve this problem, complex interdisciplinary research is needed at the junction of several related fields: AI and computational intelligence, cryptography and coding theory, information and telecommunication systems, distributed ledger technologies and blockchain applications, multimedia forensics and steganography.

The main goal of this Special Issue is to discover new scientific knowledge relevant, but not limited to, the following topics:

Cyber warfare;
5G security and privacy;
IoT security and privacy;
Quantum cryptography and quantum communications;
Post-quantum cryptography;
ML/AI applications in cybersecurity systems;
Cybersecurity application and services;
Cybersecurity systems experimentation;
Cybersecurity systems certification;
Cybersecurity systems architectures and design;
Security and networks management;
Human and society security;
Access control, authentication, privacy;
Threats, vulnerabilities, risk, formal methods;
Secure cloud computing;
Advanced cybersecurity models;
Cryptographic primitives, models and mechanisms
Lightweight computing and cryptography;

Prof. Dr. Mikolaj Karpinski
Prof. Dr. Oleksandr O. Kuznetsov
Prof. Dr. Roman Oliynykov
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 2400 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

security
cryptography
privacy
confidentiality
AI and computational intelligence techniques in cybersecurity
blockchain technology and decentralized distributed ledger
security of computer networks and systems.

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Published Papers (9 papers)

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Research

23 pages, 1115 KiB

Open AccessArticle

A Smart Contract Vulnerability Detection Method Based on Heterogeneous Contract Semantic Graphs and Pre-Training Techniques

by Jie Zhang, Gehao Lu and Jia Yu

Electronics 2024, 13(18), 3786; https://doi.org/10.3390/electronics13183786 - 23 Sep 2024

Viewed by 874

Abstract

The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic losses and data breaches, making pre-deployment vulnerability detection critically important. Traditional smart contract vulnerability detection methods suffer from low accuracy and limited reusability across different scenarios. To enhance detection capabilities, this paper proposes a smart contract vulnerability detection method based on heterogeneous contract semantic graphs and pre-training techniques. Compared to the conventional graph structures used in existing methods, heterogeneous contract semantic graphs contain richer contract information. By integrating these with pre-trained models, our method exhibits stronger vulnerability capture and generalization capabilities. Experimental results show that this method has improved the accuracy, recall, precision, and F1 value in the detection of four widely existing and harmful smart contract vulnerabilities compared with existing methods, which greatly improves the detection ability of smart contract vulnerabilities. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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11 pages, 228 KiB

Open AccessArticle

Secure Processing and Distribution of Data Managed on Private InterPlanetary File System Using Zero-Knowledge Proofs

by Kyohei Shibano, Kensuke Ito, Changhee Han, Tsz Tat Chu, Wataru Ozaki and Gento Mogi

Electronics 2024, 13(15), 3025; https://doi.org/10.3390/electronics13153025 - 31 Jul 2024

Viewed by 828

Abstract

In this study, a new data-sharing method is proposed that uses a private InterPlanetary File System—a decentralized storage system operated within a closed network—to distribute data to external entities while making its authenticity verifiable. Among the two operational modes of IPFS, public and private, this study focuses on the method for using private IPFS. Private IPFS is not open to the general public; although it poses a risk of data tampering when distributing data to external parties, the proposed method ensures the authenticity of the received data. In particular, this method applies a type of zero-knowledge proof, namely, the Groth16 protocol of zk-SNARKs, to ensure that the data corresponds to the content identifier in a private IPFS. Moreover, the recipient’s name is embedded into the distributed data to prevent unauthorized secondary distribution. Experiments confirmed the effectiveness of the proposed method for an image data size of up to 120 × 120 pixels. In future studies, the proposed method will be applied to larger and more diverse data types. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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53 pages, 26147 KiB

Open AccessArticle

Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search

by Oleksandr Kuznetsov, Nikolay Poluyanenko, Emanuele Frontoni, Sergey Kandiy, Mikolaj Karpinski and Ruslan Shevchuk

Electronics 2024, 13(10), 1825; https://doi.org/10.3390/electronics13101825 - 8 May 2024

Cited by 1 | Viewed by 812

Abstract

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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35 pages, 1496 KiB

Open AccessArticle

Augmenting Vehicular Ad Hoc Network Security and Efficiency with Blockchain: A Probabilistic Identification and Malicious Node Mitigation Strategy

by Rubén Juárez and Borja Bordel

Electronics 2023, 12(23), 4794; https://doi.org/10.3390/electronics12234794 - 27 Nov 2023

Cited by 1 | Viewed by 1981

Abstract

This manuscript delineates the development of an avant garde dual-layer blockchain architecture, which has been meticulously engineered to augment the security and operational efficacy of vehicular ad hoc networks (VANETs). VANETs, which are integral to the infrastructure of intelligent transport systems, facilitate the critical exchange of information between vehicular nodes. Despite their significance, these networks confront an array of formidable security vulnerabilities. Our innovative approach, employing a dual blockchain framework—the event chain and the reputation chain—meticulously tracks network activities, thereby significantly enhancing the trustworthiness and integrity of the system. This research presents a transformative dual-layer blockchain architecture, which was conceived to address the intricate security challenges pervasive in VANETs. The architecture pivots on a sophisticated reputation assessment framework, thus leveraging the principles of Bayesian inference and the analytical rigor of historical data to markedly diminish observational errors, as well as elevate the accuracy of reputation evaluations for vehicular nodes. A salient feature of our methodology is the implementation of an attenuation factor, which has been deftly calibrated to modulate the impact of historical behaviors on current reputation scores, thereby ensuring their relevance and alignment with recent vehicular interactions. Additionally, the numerical threshold serves as an indispensable mechanism, thus establishing a definitive criterion for the early identification of potentially malicious activities and enabling the activation of proactive security measures to safeguard the network’s integrity. Empirical validation of our dual-layer blockchain model has yielded a remarkable 86% efficacy in counteracting malevolent behaviors, thus significantly outperforming extant paradigms. These empirical outcomes underscore the model’s potential as a vanguard in the domain of secure and efficient reputation management within VANETs, thereby heralding a substantial advancement in the sphere of intelligent transportation systems. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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24 pages, 3313 KiB

Open AccessArticle

An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning

by Washington Enyinna Mbonu, Carsten Maple and Gregory Epiphaniou

Electronics 2023, 12(21), 4543; https://doi.org/10.3390/electronics12214543 - 5 Nov 2023

Cited by 2 | Viewed by 1631

Abstract

Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global model through the local training of multiple edge devices. It uses a central server for model communication and the aggregation of post-trained models. The central server orchestrates the training process by sending each participating device an initial or pre-trained model for training. To achieve the learning objective, focused updates from edge devices are sent back to the central server for aggregation. While such an architecture and information flows can support the preservation of the privacy of participating device data, the strong dependence on the central server is a significant drawback of this framework. Having a central server could potentially lead to a single point of failure. Further, a malicious server may be able to successfully reconstruct the original data, which could impact on trust, transparency, fairness, privacy, and security. Decentralizing the FL process can successfully address these issues. Integrating a decentralized protocol such as Blockchain technology into Federated Learning techniques will help to address these issues and ensure secure aggregation. This paper proposes a Blockchain-based secure aggregation strategy for FL. Blockchain is implemented as a channel of communication between the central server and edge devices. It provides a mechanism of masking device local data for secure aggregation to prevent compromise and reconstruction of the training data by a malicious server. It enhances the scalability of the system, eliminates the threat of a single point of failure of the central server, reduces vulnerability in the system, ensures security, and transparent communication. Furthermore, our framework utilizes a fault-tolerant server to assist in handling dropouts and stragglers which can occur in federated environments. To reduce the training time, we synchronously implemented a callback or end-process mechanism once sufficient post-trained models have been returned for aggregation (threshold accuracy achieved). This mechanism resynchronizes clients with a stale and outdated model, minimizes the wastage of resources, and increases the rate of convergence of the global model. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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13 pages, 1896 KiB

Open AccessArticle

Disentangled Prototypical Graph Convolutional Network for Phishing Scam Detection in Cryptocurrency Transactions

by Seok-Jun Buu and Hae-Jung Kim

Electronics 2023, 12(21), 4390; https://doi.org/10.3390/electronics12214390 - 24 Oct 2023

Cited by 1 | Viewed by 1306

Abstract

Blockchain technology has generated an influx of transaction data and complex interactions, posing significant challenges for traditional machine learning methods, which struggle to capture high-dimensional patterns in transaction networks. In this paper, we present the disentangled prototypical graph convolutional network (DP-GCN), an innovative approach to account classification in Ethereum transaction records. Our method employs a unique disentanglement mechanism that isolates relevant features, enhancing pattern recognition within the network. Additionally, we apply prototyping to disentangled representations, to classify scam nodes robustly, despite extreme class imbalances. We further employ a joint learning strategy, combining triplet loss and prototypical loss with a gamma coefficient, achieving an effective balance between the two. Experiments on real Ethereum data showcase the success of our approach, as the DP-GCN attained an F1 score improvement of 32.54%p over the previous best-performing GCN model and an area under the ROC curve (AUC) improvement of 4.28%p by incorporating our novel disentangled prototyping concept. Our research highlights the importance of advanced techniques in detecting malicious activities within large-scale real-world cryptocurrency transactions. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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21 pages, 2668 KiB

Open AccessFeature PaperArticle

Energy-Efficient Blockchain-Enabled Multi-Robot Coordination for Information Gathering: Theory and Experiments

by Cesar E. Castellon, Tamim Khatib, Swapnoneel Roy, Ayan Dutta, O. Patrick Kreidl and Ladislau Bölöni

Electronics 2023, 12(20), 4239; https://doi.org/10.3390/electronics12204239 - 13 Oct 2023

Cited by 1 | Viewed by 1312

Abstract

In this work, we propose a blockchain-based solution for securing robot-to-robot communication for a task with a high socioeconomic impact—information gathering. The objective of the robots is to gather maximal information about an unknown ambient phenomenon such as soil humidity distribution in a field. More specifically, we use the proof-of-work (PoW) consensus protocol for the robots to securely coordinate while rejecting tampered data injected by a malicious entity. As the blockchain-based PoW protocol has a large energy footprint, we next employ an algorithmically-engineered energy-efficient version of PoW. Results show that our proposed energy-efficient PoW-based protocol can reduce energy consumption by

14 %

while easily scaling up to 10 robots. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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22 pages, 830 KiB

Open AccessFeature PaperArticle

Secure and Privacy-Preserving Authentication Scheme Using Decentralized Identifier in Metaverse Environment

by Myeonghyun Kim, Jihyeon Oh, Seunghwan Son, Yohan Park, Jungjoon Kim and Youngho Park

Electronics 2023, 12(19), 4073; https://doi.org/10.3390/electronics12194073 - 28 Sep 2023

Cited by 13 | Viewed by 2422

Abstract

The metaverse provides a virtual world with many social activities that parallel the real world. As the metaverse attracts more attention, the importance of security and privacy preservation is increasing significantly. In the metaverse, users have the capability to create various avatars, which can be exploited to deceive or threaten others, leading to internal security issues. Additionally, users attempting to access the metaverse are susceptible to various external security threats since they communicate with service providers through public channels. To address these challenges, we propose an authentication scheme using blockchain, a decentralized identifier, and a verifiable credential to enable metaverse users to perform secure identity verification and authentication without disclosing sensitive information to service providers. Furthermore, the proposed approach mitigates privacy concerns associated with the management of personal information by enabling users to prove the necessary identity information independently without relying on service providers. We demonstrate that the proposed scheme is resistant to malicious security attacks and provides privacy preservation by performing security analyses, such as AVISPA simulation, BAN logic, and the real-or-random (ROR) model. We also show that the performance of our proposed scheme is better suited for the metaverse environment by providing greater security and efficiency when compared to competing schemes. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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15 pages, 599 KiB

Open AccessArticle

Malicious Contract Detection for Blockchain Network Using Lightweight Deep Learning Implemented through Explainable AI

by Yeajun Kang, Wonwoong Kim, Hyunji Kim, Minwoo Lee, Minho Song and Hwajeong Seo

Electronics 2023, 12(18), 3893; https://doi.org/10.3390/electronics12183893 - 15 Sep 2023

Cited by 2 | Viewed by 1900

Abstract

A smart contract is a digital contract on a blockchain. Through smart contracts, transactions between parties are possible without a third party on the blockchain network. However, there are malicious contracts, such as greedy contracts, which can cause enormous damage to users and blockchain networks. Therefore, countermeasures against this problem are required. In this work, we propose a greedy contract detection system based on deep learning. The detection model is trained through the frequency of opcodes in the smart contract. Additionally, we implement Gredeeptector, a lightweight model for deployment on the IoT. We identify important instructions for detection through explainable artificial intelligence (XAI). After that, we train the Greedeeptector through only important instructions. Therefore, Greedeeptector is a computationally and memory-efficient detection model for the IoT. Through our approach, we achieve a high detection accuracy of 92.3%. In addition, the file size of the lightweight model is reduced by 41.5% compared to the base model and there is little loss of accuracy. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)

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