Computers & Electrical Engineering最新文献

{"title":"Ensure authentication and confidentiality in blockchain-based IoT with cryptanalysis and machine learning in 6G-enabled heterogeneous IoT-Blockchain","authors":"Dr. Bharati B Pannyagol ,&nbsp;Dr. Santosh L Deshpande","doi":"10.1016/j.compeleceng.2025.110303","DOIUrl":"10.1016/j.compeleceng.2025.110303","url":null,"abstract":"<div><div>Presently, the Internet of Things (IoT) is considered the major network, which includes multiple intelligent devices for interacting and transferring data throughout the Internet. The IoT is applicable in commercial, military, and industrial applications. With the popularity of IoT devices, security is the main issue for data privacy. Hence, authentication and secure data transmission are crucial parameters for the secure communication of IoT devices. Moreover, blockchain is employed for providing efficient data sharing and secure authentication in IoT. This research proposes authentication based on data protection (DPro_Auth) for blockchain-based IoT. Entities like users, IoT devices, blockchain, and a smart contract are utilized to attain authentication and confidentiality. The steps like initialization, registration, key generation, authentication, data verification, and access control are carried out. The initialization is based on the physical unclonable function-based chaotic pseudorandom number generators (PUF-CPRNG). Moreover, the pseudorandom number generator-based pseudo-randomly enhanced logistic map (PRNG–PELM) is employed in the key generation process. In addition, the memory, and computational time are used to validate the DPro_Auth, with the finest outcome of 5.277 MB, and 0.256 s are obtained.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110303"},"PeriodicalIF":4.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"G-VAE: Variational autoencoder-based adversarial attacks and defenses in industrial control systems","authors":"Lijuan Xu,&nbsp;Zhi Yang,&nbsp;Dawei Zhao,&nbsp;Fuqiang Yu,&nbsp;Yang Zhou,&nbsp;Hu Zhang","doi":"10.1016/j.compeleceng.2025.110290","DOIUrl":"10.1016/j.compeleceng.2025.110290","url":null,"abstract":"<div><div>The industrial control domain is increasingly focused on addressing the cybersecurity challenges posed by adversarial sample attacks. A key difficulty in such attacks on industrial control systems (ICS) is the failure to account for the complex dependencies among various features, making it challenging to learn the relationships between multiple sensors and establish constraints for representing multidimensional data in this domain. Additionally, defending against adversarial samples is hindered by the existence of multiple detection methods and the challenge of creating a defense model without being aware of adversarial samples beforehand. To tackle these challenges, this paper proposes a gated recurrent unit (GRU)-based variational autoencoder (VAE) method for both attacking and defending against adversarial samples. Our approach involves training a GRU model to understand the intrinsic interactions among sensors and then adding perturbations to generate adversarial samples that adhere to feature constraints. On the defense side, we introduce a VAE Feature Weight (VAE-FW) method, which operates without explicit information about the adversarial samples. To make sure that characteristics with the worst prediction outcomes do not dominate anomaly scores in VAE-FW, we equalize the prediction errors across various features. Experiments conducted on three real-world sensor datasets demonstrate that our adversarial attack method significantly enhances attack efficiency while confirming its effectiveness. Furthermore, our defense method, VAE-FW, detects anomalies with greater accuracy than current baseline anomaly detection methods, achieving an average increase of 28.8% in AUC values.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110290"},"PeriodicalIF":4.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Paradigm of Hallucinations in AI-driven cybersecurity systems: Understanding taxonomy, classification outcomes, and mitigations","authors":"Aditya K Sood ,&nbsp;Sherali Zeadally ,&nbsp;EenKee Hong","doi":"10.1016/j.compeleceng.2025.110307","DOIUrl":"10.1016/j.compeleceng.2025.110307","url":null,"abstract":"<div><div>The adoption of AI to solve cybersecurity problems is occurring exponentially. However, AI-driven cybersecurity systems face significant challenges due to the impact of hallucinations in Large Language Models (LLMs). In AI-driven cybersecurity systems, hallucinations refer to instances when an AI model generates fabricated, inaccurate, and misleading information that impacts the security posture of organizations. This failure to recognize and misreport security threats identifies benign activities as malicious, invents insights not grounded to actual cyber threats, and causes real threats to go undetected due to erroneous interpretations. Hallucinations are a critical problem in AI-driven cybersecurity because they can lead to severe vulnerabilities, erode trust in automated systems, and divert resources to address non-existent threats. In cybersecurity, where real-time, accurate insights are vital, hallucinated outputs—such as mistakenly generated alerts, can cause a misallocation of time and resources. It is crucial to address hallucinations by improving LLM accuracy, grounding outputs in real-time data, and implementing human oversight mechanisms to ensure that AI-based cybersecurity systems remain trustworthy, reliable, and capable of defending against sophisticated threats. We present a taxonomy of hallucinations in LLMs for cybersecurity, including mapping LLM responses to classification outcomes (confusion matrix components). Finally, we discuss mitigation strategies to combat hallucinations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110307"},"PeriodicalIF":4.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fused multi-level attention features with a constraint fusion network for colorectal tissue classification using histopathological images","authors":"Rashi Chauhan ,&nbsp;Karnati Mohan ,&nbsp;Pradeep Singh","doi":"10.1016/j.compeleceng.2025.110296","DOIUrl":"10.1016/j.compeleceng.2025.110296","url":null,"abstract":"<div><div>Colorectal cancer (CRC) has a significant mortality rate and continues to damage human lives around the world. Early diagnosis/identification of CRC helps to lengthen human life and detour infection. Histopathological examination is a standard method for diagnosing and detecting CRC. Visual inspection of histopathological diagnoses takes longer, and the outcome is based on physicians’ subjective perceptions. Current approaches mainly rely on various varieties of texture characteristics. Moreover, deep learning methodologies’ categorization efficacy surpasses that of medical practitioners. Nevertheless, elevated complexity would diminish the practicality of these strategies. This study employs a unique method known as fused multi-level attention features with a constraint fusion network (FMANet) for accurately classifying colorectal tissue using histopathological images. FMANet comprises modified dilated residual inception feature extraction, channel-wise, and spatial attention components. These components assist in extracting the most pertinent and prejudiced characteristics from the low-level (LL) and high-level (HL) features in the HIs. The constraint fusion technique is employed to weigh and fuse LL and HL attributes adaptively. The proposed model is evaluated by the number of parameters, accuracy, precision, recall, F1 score, and area under the curve. Moreover, the proposed FMANet attained an accuracy of 95% on the colorectal histology dataset and 97% on National Center for Tumor-CRC-hematoxylin &amp; eosin-100K. The proposed method outperforms ten state-of-the-art algorithms on two datasets, as a result, it is believed that this strategy has great potential to aid physicians in clinical diagnoses and reduce the number of experiments by using computer-aided diagnosis to classify CRC cells. The complete system’s source code will be accessible at <span><span>https://github.com/KarnatiMOHAN/FMANet_colorectal-tissue_classification</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110296"},"PeriodicalIF":4.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble learning unlocking point load forecasting accuracy: A novel framework based on two-stage data preprocessing and improved multi-objective optimisation strategy","authors":"Jingmin Luan ,&nbsp;Qiyang Li ,&nbsp;Yuyan Qiu ,&nbsp;Weican Liu","doi":"10.1016/j.compeleceng.2025.110282","DOIUrl":"10.1016/j.compeleceng.2025.110282","url":null,"abstract":"<div><div>Accurate point power forecasts are critical for maintaining the security and stability of the grid. However, Load data is volatile and difficult to predict with high accuracy. To improve the stability and accuracy of the model, we proposed a novel hybrid load forecasting framework consisting of three modules. Firstly, we used <em>Successive Variational Mode Decomposition (SVMD)</em> in the data preprocessing module to extract trend features and denoise the data. To reduce the negative impact of feature redundancy on model predictions, we employed feature selection to identify four essential features to aid model training. Secondly, in the ensemble learning module, we address the limitations of single predictive models by combining the models <em>Back Propagation (BP)</em>, <em>Temporal Convolutional Network (TCN)</em>, <em>Bidirectional Long Short-Term Memory (BiLSTM)</em>, <em>Bidirectional Gated Recurrent Unit (BiGRU)</em> and <em>Transformer</em>. Finally, in the improved multi-objective optimisation algorithm module, we implemented various strategies to enhance the optimisation algorithm. We designed some experiments using three load datasets from Australia. The results demonstrated that the mean absolute percentage error values are below <strong><em>1.25%</em></strong>, with the best value reaching <strong><em>0.9256%</em></strong>. In contrast, the best result for the mean absolute percentage error of the baseline models was <strong><em>1.4431%</em></strong> in New South Wales. This represents a <strong><em>35.9%</em></strong> improvement in load forecasting performance with our proposed model, highlighting its superior accuracy compared to competing approaches. It shows that our forecasting framework is far better than other rivals.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110282"},"PeriodicalIF":4.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure lightweight digital twin (DT) technology for seamless wireless communication in vehicular ad hoc network","authors":"M.K. Kishore ,&nbsp;V. Gajendra Kumar ,&nbsp;B. Nancharaiah","doi":"10.1016/j.compeleceng.2025.110291","DOIUrl":"10.1016/j.compeleceng.2025.110291","url":null,"abstract":"<div><div>Security in VANETs (Vehicular Ad Hoc Networks) is crucial due to the inherent vulnerabilities in wireless communication. These networks face several security challenges, including authentication, privacy protection, and secure data transmission. Authentication issues arise from the dynamic nature of vehicular networks, where vehicles need to authenticate each other and entities like roadside units securely. The challenges include GPS spoofing, where attackers manipulate location data to mislead vehicles; Sybil attacks, where a single node illegitimately claims multiple identities to disrupt network operations; and Denial of Service (DoS) attacks, which overwhelm the network, rendering it unavailable for legitimate users. Additionally, key management for secure encryption, replay attacks where previously transmitted messages are maliciously reused, and jamming attacks that disrupt wireless signals are notable threats. To overcome the issue in VANET wireless communication this paper proposed DT-LHBC (Digital Twin Lightweight Hashing Blockchain Cryptography) into VANETs (Vehicular Ad-Hoc Networks) to enhance security and efficiency in vehicular communication systems. DT-LHBC combines advanced cryptographic techniques with digital twin technology to ensure secure data transmission and communication integrity across dynamic vehicular environments. With the implementation of a digital twin technology cryptography model of hashing-based blockchain (LHBC) is integrated to achieve effective wireless communication. Results demonstrate DT-LHBC's effectiveness in mitigating various security threats, including Sybil attacks, Denial of Service (DoS) attacks, and Message Tampering, achieving up to 98 % accuracy in attack detection. Despite challenges in optimizing cryptographic speeds and addressing GPS Spoofing vulnerabilities, DT-LHBC reduces blockchain latency to as low as 12 milliseconds and maintains a high transaction throughput of 500 transactions per second.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110291"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HardSecUAV: A hardware-based mutual authentication protocol for network of drones","authors":"Anubhav Elhence ,&nbsp;Vinay Chamola","doi":"10.1016/j.compeleceng.2025.110286","DOIUrl":"10.1016/j.compeleceng.2025.110286","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV) networks are increasingly utilized in various applications, yet they face significant security challenges due to their open operational environments and resource constraints. Existing authentication protocols often rely on stored secret keys, making them vulnerable to physical attacks and key compromise. To address this research gap, we propose <em>HardSecUAV</em>, a lightweight and secure authentication protocol that leverages hardware-based security features of the DS28S60 cryptographic coprocessor, including Physically Unclonable Functions (PUFs). Our protocol eliminates the need for secret key storage by generating device-specific secrets on-demand, enhancing resistance to physical attacks. Implementation results demonstrate that <em>HardSecUAV</em> achieves a total computation time of approximately <strong>28.775</strong> <span><math><mrow><mi>μ</mi><mi>s</mi></mrow></math></span> during the authentication phase, suitable for real-time operations. The storage requirement is <strong>1024 bits</strong>, and the communication overhead is <strong>1664 bits</strong>, comparable to existing schemes. Compared to existing protocols, <em>HardSecUAV</em> provides enhanced security features, including mutual authentication, replay attack resistance, forward secrecy, and UAV-to-UAV authentication without dependence on Ground Station(GS), making it a robust solution for securing UAV networks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110286"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-domain recommender systems via multimodal domain adaptation","authors":"Adamya Shyam ,&nbsp;Ramya Kamani ,&nbsp;Venkateswara Rao Kagita ,&nbsp;Vikas Kumar","doi":"10.1016/j.compeleceng.2025.110300","DOIUrl":"10.1016/j.compeleceng.2025.110300","url":null,"abstract":"<div><div>Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized recommendations. CF techniques, especially for newly launched platforms, often face a critical issue known as the data sparsity problem, which greatly limits their performance. Cross-domain CF alleviates the problem of data sparsity by finding a common set of entities (users or items) across the domains, which then act as a conduit for knowledge transfer. Nevertheless, most real-world datasets are collected from different domains, so they often lack information about anchor points or reference information for entity alignment. This paper introduces a domain adaptation technique to align the embeddings of entities across domains. Our approach first exploits the available textual and visual information to independently learn a multi-view latent representation for each entity in the auxiliary and target domains. The different representations of the entity are then fused to generate the corresponding unified representation. A domain classifier is then trained to learn the embedding for the domain alignment by fixing the unified features as the anchor points. Experiments on four publicly available benchmark datasets indicate the effectiveness of our proposed approach.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110300"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven recursive multivariable modeling, operation, and control of active distribution networks with distributed generation","authors":"Diego Feroldi ,&nbsp;Pablo Rullo ,&nbsp;Sair Rodríguez del Portal,&nbsp;Lautaro Braccia ,&nbsp;Patricio Luppi ,&nbsp;David Zumoffen","doi":"10.1016/j.compeleceng.2025.110241","DOIUrl":"10.1016/j.compeleceng.2025.110241","url":null,"abstract":"<div><div>This paper addresses the challenges arising from the increasing integration of distributed generation into active distribution networks (ADNs), focusing on their modeling, operation, and control. Data-driven recursive multivariable modeling, capable of capturing both static and dynamic interactions in real time, has emerged as a promising solution. By utilizing the extensive data generated by modern grid infrastructure, this approach enhances network model accuracy and improves operational efficiency and control strategies. This paper strengthens the connection between Process Systems Engineering (PSE) and power systems, traditionally underexplored in this domain. By integrating PSE principles, particularly data-driven and control allocation methodologies, into the modeling, operation, and control of ADNs, this work optimizes power system performance. Three Recursive Partial Least Squares (RPLS) methodologies—sample-wise, block-wise, and moving-window—are rigorously compared regarding estimation/prediction characteristics and convergence speed. This novel analysis challenges the assumption of instantaneous model adaptation, emphasizing the importance of carefully considering convergence periods for effective monitoring, control, and optimization. The paper proposes and analyzes three control structures integrated into an RPLS-based supervisory strategy for voltage regulation at ADN nodes: (1) decentralized control, (2) control allocation with measurement combination, and (3) optimization-based centralized control. Different integration formats are evaluated based on the controller technology used: (a) simple setpoint updates, (b) full ADN model adaptation to recalculate controller matrices, and (c) full model adaptation for updating the optimization formulation. Simulation results were obtained using the IEEE 33-bus test system. The results reveal a trade-off between the complexity and performance benefits of each control strategy. Although no strategy proves definitively superior, the latter two show more promising overall prospects.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110241"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PrGChain: A privacy-preserving blockchain-enabled energy trading system","authors":"Ahmed-Sami Berkani ,&nbsp;Hamouma Moumen ,&nbsp;Saber Benharzallah ,&nbsp;Mohand Tahar Kechadi ,&nbsp;Ahcène Bounceur","doi":"10.1016/j.compeleceng.2025.110289","DOIUrl":"10.1016/j.compeleceng.2025.110289","url":null,"abstract":"<div><div>The integration of blockchain, Internet of Things devices, and distributed energy resources is revolutionizing peer-to-peer energy trading by enabling decentralized, efficient, and transparent transactions. However, existing solutions face challenges related to privacy, interoperability, and scalability. This paper presents PrGChain, a privacy-preserving blockchain-enabled energy trading framework within the smart grid, incorporating a decentralized ZKOracle to securely connect blockchain networks with off-chain energy data sources.</div><div>The proposed ZKOracle employs zero-knowledge proofs to verify energy data without exposing sensitive information, ensuring compliance with privacy regulations, while leveraging a distributed network of oracle nodes for enhanced reliability and interoperability. To improve security and efficiency, PrGChain utilizes smart contracts, decentralized applications (dApps), and leverages stablecoins to mitigate cryptocurrency volatility.</div><div>Performance evaluations demonstrate that our system achieves improved decentralization and privacy without sacrificing efficiency. This is particularly true when deployed on Layer 2 blockchain networks like Polygon, where transaction latency and costs are significantly reduced.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110289"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}