Computers & Electrical Engineering最新文献

{"title":"Unveiling Android security testing: A Comprehensive overview of techniques, challenges, and mitigation strategies","authors":"Durga Viswanath Palutla ,&nbsp;Sriramulu Bojjagani ,&nbsp;Sai Charan Reddy Mula ,&nbsp;Ravi Uyyala ,&nbsp;Neeraj Kumar Sharma ,&nbsp;Mahesh Kumar Morampudi ,&nbsp;Muhammad Khurram Khan","doi":"10.1016/j.compeleceng.2025.110620","DOIUrl":"10.1016/j.compeleceng.2025.110620","url":null,"abstract":"<div><div>With the rapid growth of Android applications, ensuring robust security has become a critical concern. Traditional Vulnerability Assessment and Penetration Testing (VAPT) approaches, though effective across platforms, often fall short in addressing Android-specific security challenges. This paper presents a comprehensive review of security testing methods tailored to the Android ecosystem, including static and dynamic analysis, hybrid approaches, network communication testing, reverse engineering, malware detection, and permission-based assessments. Android’s open-source nature, device fragmentation, and inconsistent security policies introduce unique vulnerabilities that require specialized testing strategies. By examining current tools, methodologies, and best practices, this review identifies recurring gaps in the Android application security testing process. It highlights the need for more adaptable and thorough testing frameworks. The insights provided are valuable to developers, researchers, and security professionals aiming to strengthen Android app security. Ultimately, this work underscores the importance of tailoring security assessment practices to the evolving threat landscape of the Android platform, thereby contributing to the development of safer and more resilient applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110620"},"PeriodicalIF":4.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889513","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":"Pool Free Rapid Segmentation Network (PFRS-Net) to detect human blastocyst compartments for embryonic assessment","authors":"Abida Hussain ,&nbsp;Adnan Haider ,&nbsp;Saima Ashraf ,&nbsp;Syed Muhammad Ali Imran ,&nbsp;Muhammad Arsalan","doi":"10.1016/j.compeleceng.2025.110636","DOIUrl":"10.1016/j.compeleceng.2025.110636","url":null,"abstract":"<div><div>Assisted reproductive technology has become an increasingly popular solution to address infertility in humans, primarily by in vitro fertilization (IVF). IVF is a complex process where eggs and sperm are combined outside the human body. This occurs in a controlled, specialized laboratory setting that supports and encourages the growth of embryos before they are transferred to the uterus. The IVF process is carefully monitored and regulated within a laboratory environment until the embryos develop and progress to the blastocyst stage. The standard procedure for in vitro fertilization (IVF) involves transferring one or two blastocysts from a batch that has been developed under controlled conditions. A detailed morphological analysis of these blastocysts is performed, assessing their distinct components, including the trophectoderm (TE), zona pellucida (ZP), inner cell mass (ICM), and blastocoel (BL), using manual microscopic techniques. Although deep learning has been successfully utilized in various medical diagnostic and analytical applications, its integration for automating the morphological analysis of human blastocysts continues to face several obstacles. Current methodologies often exhibit inaccuracies and necessitate considerable preprocessing along with expensive computational architectures. As a result, further research is needed to improve the accuracy and efficiency of deep learning techniques in this field to enable their full potential in assisted reproductive technology. To address this challenge, we introduce the Pool Free Rapid Segmentation Network (PFRS-Net), which is specifically developed to effectively identify the compartments of human blastocysts without relying on pooling operations. The network utilizes rapid convolutional block (RCB) modules to achieve accurate detection. The RCB module is specifically designed to capture valuable deep features with computational efficiency. The Swift Decoder block is then used to up-sample the feature maps to their original size using a few layers. This specialized design helps to reduce the number of trainable parameters while maintaining high segmentation accuracy and recovering the lost spatial information using a feature enhancement block (FEB). Our proposed PFRS-Net accurately detects the blastocyst compartments without preprocessing the image and consuming 1.1 million trainable parameters only. This method is trained and tested using a publicly accessible dataset of human blastocyst images. The experimental outcomes demonstrate superior segmentation performance in detecting blastocyst components, which is vital for embryonic research and analysis.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110636"},"PeriodicalIF":4.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889511","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 and scalable trust management in IoT: A hierarchical blockchain-based approach","authors":"Elham Meybodian ,&nbsp;Seyedakbar Mostafavi ,&nbsp;Tooska Dargahi ,&nbsp;Vahid Ranjbar Bafghi","doi":"10.1016/j.compeleceng.2025.110631","DOIUrl":"10.1016/j.compeleceng.2025.110631","url":null,"abstract":"<div><div>The diverse and resource-constrained nature of Internet of Things (IoT) devices make them vulnerable against various security attacks. Effective trust management within the IoT ecosystem is crucial for reliable data collection and sharing, as well as the detection of malicious nodes. Centralized trust management methods are inefficient due to several challenges, including single point of attack/failure, unauthorized manipulation of trust data, resource limitations of smart devices, and scalability issues. Blockchain technology provides a suitable solution for trust management due to its decentralization, transparency, and immutability features. However, deploying blockchain for IoT devices is not simple due to the low performance and high computational costs of consensus algorithms, limited resources of smart devices, and the large volume of transactions created by nodes. In this paper, a hierarchical trust management approach based on blockchain is proposed. The proposed approach evaluates each node’s reputation and organizational trust at both intra-organizational and inter-organizational levels. At the internal level, a lightweight blockchain is used to evaluate and store the trust score of the nodes. At the inter-organizational level, interactions between organizations and their trust level are recorded in the public blockchain. Two methods are proposed, i.e. probing-based and evidence-based, for evaluating the reputation of each node and the trust level of each organization. The evaluation results show that with a maximum of 35% malicious nodes within an organization, the proposed method can correctly identify the malicious and honest nodes. The recall and specificity measures obtained are both greater than 0.9. Additionally, organizations with more than 35% of malicious nodes are blacklisted and suspended.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110631"},"PeriodicalIF":4.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879314","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":"An efficient essential secret sharing: Application to gray and color images","authors":"Ramakant Kumar ,&nbsp;Avishek Adhikari ,&nbsp;Sahadeo Padhye ,&nbsp;Mainejar Yadav","doi":"10.1016/j.compeleceng.2025.110630","DOIUrl":"10.1016/j.compeleceng.2025.110630","url":null,"abstract":"<div><div>A Secret Sharing Scheme (SSS) is a cryptographic primitive used to share secrets, such that the secret can be revealed only when a specified number of shares are available. Due to varying levels of importance or hierarchy among shareholders, some shareholders may be deemed essential for secret recovery. This concept is addressed by essential secret sharing. The essential SSSs designed for sharing secret images suffer from various limitations, including different shadow image sizes for essential and non-essential shareholders, large share sizes, preprocessing requirements, the need for concatenating sub-shadows, constraints on image type, and pixel expansion. To address these issues, we propose an essential SSS based on simple linear algebra. This scheme is both perfect and ideal. We then leverage this scheme to develop a perfect and ideal essential secret image sharing (SIS) scheme that accommodates essential participants. We further modify the scheme to reduce the share sizes by a factor of k, which makes it efficient. It is applicable for gray as well as color images. Our approach relies on simple operations like matrix addition, multiplication, and inversion. Our scheme supports color images and offers lossless recovery. It overcomes the limitations present in existing SIS schemes. We have discussed experimental results for the gray and color images. Furthermore, all schemes presented in this article are quantum secure.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110630"},"PeriodicalIF":4.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879313","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":"Extraction and encryption of facial information for government personnel: STP-CS with Tri-color shuffling scheme","authors":"Linlin Tan ,&nbsp;Suo Gao ,&nbsp;Nanrun Zhou ,&nbsp;Yinghong Cao ,&nbsp;Jun Mou","doi":"10.1016/j.compeleceng.2025.110624","DOIUrl":"10.1016/j.compeleceng.2025.110624","url":null,"abstract":"<div><div>Facial recognition security is a critical concern in the government sector. Protecting individuals’ facial information is essential for ensuring the confidentiality of documents accessed via facial recognition, thereby preventing theft and damage. This paper proposes a facial image protection scheme based on a semi-tensor product compressed sensing (STP-CS) and tri-color shuffling scheme (TSS). First, facial information is extracted and compressed to reduce the data volume required for encryption. Then, the chaotic sequences generated by the Autapse Aihara neuron (AAN) map, combined with TSS, are used to encrypt the facial information of all authorized personnel. Finally, simulation tests and performance analyses validated the feasibility and security of the proposed encryption scheme, effectively protecting the facial information from theft and misuse.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110624"},"PeriodicalIF":4.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879239","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":"Detection and extraction of optimal features from power quality disturbances based on wavelet coefficient reconstruction and noise-robust methods","authors":"Eilen García Rodríguez ,&nbsp;Enrique Reyes Archundia,&nbsp;Jose A. Gutiérrez Gnecchi,&nbsp;Oscar I. Coronado Reyes,&nbsp;Juan C. Olivares Rojas,&nbsp;Arturo Méndez Patiño","doi":"10.1016/j.compeleceng.2025.110615","DOIUrl":"10.1016/j.compeleceng.2025.110615","url":null,"abstract":"<div><div>The Discrete Wavelet Transform (DWT) is a well-established technique for detecting power quality disturbances, particularly transients. However, its practical implementation is often limited by its high sensitivity to noise. The downsampling process in Multiresolution Analysis (MRA) introduces aliasing distortion, which can be mitigated by carefully selecting decomposition and reconstruction filters. As a result, the accurate reconstruction of approximation and detail components is achieved, facilitating the extraction of key features essential for disturbance classification. This study proposes a noise-robust methodology that effectively filters noise while preserving interference characteristics for detecting disturbances with magnitude variations, such as sags, swells, and interruptions. These disturbances exhibit similar spectral and duration characteristics, making them difficult to distinguish from the pure signal, which has a fundamental frequency of 60 Hz and a magnitude variation of <span><math><mrow><mo>+</mo><mo>−</mo><mn>10</mn><mtext>%</mtext></mrow></math></span>. The approach employs DWT with MRA, followed by the reconstruction of approximation coefficients using the Inverse Discrete Wavelet Transform (IDWT). Feature extraction methods, including peak detection via local maxima identification, are combined with noise-robust techniques like Shannon entropy and energy to detect oscillatory transients, harmonics, flicker, notches, and complex disturbances, such as sag and swell with harmonics and sag and swell with oscillatory transients. The extracted feature vectors, consisting of eight elements, were used as input for six machine learning classifiers. Experimental results demonstrate that the proposed detection and feature extraction techniques achieve classification percentages exceeding 99%, even under varying noise conditions, allowing for the differentiation of each disturbance type. Most importantly, they enable clear distinction from the pure signal.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110615"},"PeriodicalIF":4.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879312","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":"A novel feature engineering method for network anomaly detection","authors":"Shaojie Han ,&nbsp;Feiyu Li ,&nbsp;Xueqiang Han ,&nbsp;Shihui Zhang","doi":"10.1016/j.compeleceng.2025.110627","DOIUrl":"10.1016/j.compeleceng.2025.110627","url":null,"abstract":"<div><div>Network anomaly detection leverages machine learning and statistical methods to identify deviations from normal network behavior. However, existing approaches often struggle to detect various types of anomalies due to the rapidly changing nature of traffic patterns, which are influenced by factors such as time, environment, and demand, even when the data itself remains consistent. To address this challenge, we propose a novel feature engineering method called THA-RNCT, which integrates a Two-way Hybrid Analysis (THA) strategy with a Region-based Noisy Concatenation Transformation (RNCT) module. Specifically, the THA strategy combines statistical techniques with classifier analysis to reduce the computational load on the classifier while ranking the feature importance. Additionally, the RNCT module transforms one-dimensional traffic features into two-dimensional images, enabling a convolutional neural network model to achieve high accuracy in anomaly detection. Extensive experiments on CIC-IDS2018 dataset demonstrate that the proposed method not only achieves superior performance, but also has many advantages such as lightweight, holism, and strong anti-interference ability.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110627"},"PeriodicalIF":4.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863784","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":"Enhanced cybersecurity for smart grids: Detecting protocol-specific DDoS attacks on Modbus networks","authors":"Sania Kanwal ,&nbsp;Waqas Amin ,&nbsp;Abdullah Aman Khan ,&nbsp;Bilal Rafique ,&nbsp;Qi Huang ,&nbsp;Li Jian ,&nbsp;Iqra Batool","doi":"10.1016/j.compeleceng.2025.110629","DOIUrl":"10.1016/j.compeleceng.2025.110629","url":null,"abstract":"<div><div>Smart grid (SG) infrastructure is critical in developing distributed energy networks. However, the increase in communication among the several entities of the SG also leads to an increase in vulnerabilities. Among the several attacks, Distributed Denial of Service (DDoS) is the most common threat that can disrupt the normal functioning of SGs, causing instability and severe security issues across the entire grid. While many researchers have explored Machine Learning (ML) and Deep Learning (DL) solutions to enhance SG security, most of them detect DDoS attacks using datasets that do not include SG-specific protocols, such as the Modicon Communication Bus (Modbus) protocol. The proposed study presents a novel contribution by generating a Modbus-specific DDoS attack dataset for SG environments and applying a DL Sparse AutoEncoder (SAE)-based method to detect such attacks. The experimental results show that the proposed model detects DDoS attacks in the SG network with an Accuracy of 76%, compared to state-of-the-art methods such as Random Forest (RF), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU), with Accuracies of 73% and 71%, respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110629"},"PeriodicalIF":4.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863782","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":"A DID-based one-time session key authentication mechanism for secure human-AI chatbot communication","authors":"Wooyoung Son ,&nbsp;Soonhong Kwon ,&nbsp;Jong-Hyouk Lee","doi":"10.1016/j.compeleceng.2025.110622","DOIUrl":"10.1016/j.compeleceng.2025.110622","url":null,"abstract":"<div><div>As generative Artificial Intelligence (AI) technology has recently gained popularity, society is undergoing a full-scale transformation centered around AI. This technology is attracting attention across various fields, particularly as it meets the growing demand for ‘24/7 availability’. Among these applications, AI chatbot-based Robotic Process Automation (RPA) systems have demonstrated the ability to automate tasks, and with the integration of generative AI, they can now handle more advanced operations such as sending emails and managing complex workflows. However, because AI chatbot-based RPA systems are required to perform sensitive and high-level tasks, secure identity authentication is essential. Traditional Public Key Infrastructure (PKI)-based authentication mechanisms pose risks, as they often require storing personal information within the AI chatbot system—potentially increasing the damage in the event of a security breach. To address this issue, this paper proposes an authentication mechanism that uses a Decentralized Identity (DID)-based one-time session key. By leveraging DID technology, the proposed mechanism ensures self-sovereignty and privacy. Furthermore, the use of a one-time session key guarantees session independence, non-reusability, and untraceability. A performance comparison with PKI-based mechanisms shows that when more than five authentications are performed, the proposed mechanism achieves higher time efficiency, highlighting its advantages in both security and effectiveness. Additionally, potential security threats in each step of the proposed system are analyzed probabilistically. A mathematical formula is presented to demonstrate that the likelihood of such threats occurring is very low. By performing partial differentiation on the attack success probability with respect to representative variables at each step, the analysis identifies which authentication process most significantly influences overall system security. This provides clear insights for designing secure authentication systems based on the proposed approach.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110622"},"PeriodicalIF":4.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863783","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":"Optimal integration of biomass distributed generators for enhancing the reliability and security of the distribution systems using reliable secretary bird optimization algorithm","authors":"Hossam Hassan Ali ,&nbsp;Mohamed Hashem ,&nbsp;Ahmed Fathy ,&nbsp;Mohamed Ebeed ,&nbsp;Mohamed Khamies","doi":"10.1016/j.compeleceng.2025.110621","DOIUrl":"10.1016/j.compeleceng.2025.110621","url":null,"abstract":"<div><div>This paper proposes a new methodology incorporating the secretary bird optimization algorithm (SBOA) to decide the optimal placements, power factors, and capacities of biomass distributed generators (BDGs) in electric distribution systems (EDSs) for enhancing their techno-economic performance involving various classifications of reliability indicators as well as the system security. Because of its improved exploration/exploitation balance, the SBOA can avoid local solutions and achieve better convergence performance. A weighted fitness function to be minimized including energy not supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), system line loading index (LLI), and total net present cost (TNPC) is proposed. Moreover, the uncertainty effect for the commercial, industrial, and residential voltage-dependent load demand (VDLD) models as well as the impact of protective devices are considered. The analysis is performed on two EDSs involving the IEEE-69 bus and the IEEE-118 bus over 24 hours. The suggested approach is assessed via conducting comparison to other recent metaheuristic approaches. The captured results demonstrated the effectiveness of the suggested SBOA in deciding the best locations, power factors, and capacities of the installed biomass DGs. The outcomes validated the suggested SBOA's robustness and efficacy in resolving the addressed issue.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110621"},"PeriodicalIF":4.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863781","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}