Computers & Electrical Engineering最新文献

{"title":"Dual-SPIR model for predicting APT malware spread in organization networks","authors":"Hai Anh Tran ,&nbsp;Xuan Cho Do ,&nbsp;Thanh Thuy Nguyen","doi":"10.1016/j.compeleceng.2025.110214","DOIUrl":"10.1016/j.compeleceng.2025.110214","url":null,"abstract":"<div><div>Modeling the spread of Advanced Persistent Threat (APT) malware in systems is currently an important task. Several compartmental models have been proposed, and they have shown some effectiveness, indicating this is a promising research direction. However, these approaches still face some key challenges, including: i) they have not yet fully modeled the lifecycle and processes of APT malware; ii) they have not yet calculated or identified the influence of environmental factors on predicting the malware spread. To address these two issues, this paper introduces a new model called a Dual Susceptible-Protected-Infected-Recovered (Dual-SPIR) model. For the first issue, the proposed Dual-SPIR model will be a two-layer model that represents the spread, privilege escalation, and data theft process of APT malware. To address the second issue, this research proposes three main factors that affect the spread of APT malware, including: i) the behavior of the malware; ii) the security technologies used by the system; and iii) system vulnerabilities. The Dual-SPIR model will calculate the impact of these three factors on the spread of APT malware within the system. Specifically, for malware behavior, we suggest using the MITRE ATT&amp;CK Framework, which is currently one of the best tools for defining APT attack strategies and tactics. For system protection, we selected antivirus software, a widely used tool by organizations to protect their systems from APT campaigns. Lastly, for system vulnerabilities, the research focuses on office software vulnerabilities in the Windows 10 operating system. Different scenarios have shown that the Dual-SPIR model in this paper performs better than other approaches across all evaluation metrics. This demonstrates that the research not only has academic value but also practical relevance, as it successfully combines three key factors to model the spread of APT malware within systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110214"},"PeriodicalIF":4.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529564","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":"Automatic emergency obstacle avoidance for intelligent vehicles considering driver-environment risk evaluation","authors":"Xiaodong Wu,&nbsp;Chengrui Su,&nbsp;Zhouhang Yu,&nbsp;Sheng Zhao,&nbsp;Hangyu Lu","doi":"10.1016/j.compeleceng.2025.110187","DOIUrl":"10.1016/j.compeleceng.2025.110187","url":null,"abstract":"<div><div>Obstacle avoidance is crucial for driving safety, especially in curve road scenarios. To improve the driving safety, this paper proposes an automatic emergency obstacle avoidance strategy for intelligent vehicles with integrated consideration of the driver risk and environment risk evaluation. First, a framework for driver risk evaluation based on distraction detection and driver body pose estimation is proposed. Driver risk is obtained by fusing the pose deviation level obtained by BlazePose and the distraction type detected based on Swin Transformer. Then, an adaptive driving risk evaluation model based on Gaussian model is established by analyzing the characteristics of curve road, which can accurately describe the curve road risk distribution. Subsequently, an automatic emergency obstacle avoidance strategy integrating driver-environment risk is established based on the human-machine cooperative driving pattern and game theory. The cooperative path planning provides safe obstacle avoidance paths. Finally, driver-in-the-loop experiments are conducted to validate the effectiveness of the proposed strategy in curve road scenarios. The results demonstrate that the proposed strategy has superior performance than other advanced cooperative driving strategy in improving driving safety, reducing tracking error, and enhancing vehicle stability and driving comfort, etc.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110187"},"PeriodicalIF":4.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529493","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":"Efficient and secure integration of renewable energy sources in smart grids using hybrid fuzzy neural network and improved Diffie-Hellman key management","authors":"E. Vignesh,&nbsp;P. Aruna Jeyanthy","doi":"10.1016/j.compeleceng.2025.110206","DOIUrl":"10.1016/j.compeleceng.2025.110206","url":null,"abstract":"<div><div>The smart grid signifies a sophisticated Cyber Physical System (CPS) that merges the power grid infrastructure with modern Information and Communication Technologies (ICT). However, the increasing dependence on ICT makes smart grid system vulnerable to cyber threat. Therefore, it is crucial to implement robust security measures to protect CPS of smart grid for ensuring reliable and uninterrupted operation. This paper introduces an efficient routing and security approaches using deep learning and key management technique to incorporate cyber security measures against attacks in smart grid system. This comprehensive framework integrates Hybrid Renewable Energy Sources (HRES), into smart grid system, including the combination of Photovoltaic (PV) system, wind turbines and battery. The HRES smart grid system is incorporated with ICT, allowing for real-time monitoring, management and optimization of electricity consumption and distribution. To facilitate efficient transmission of data this research proposes a hybrid system combining Fuzzy Neural Network (FNN) optimized using Falcon Optimization Algorithm (FOA). This ensures, effective data routing, resulting in enhanced energy efficiency and network lifetime. Furthermore, the proposed smart grid system incorporates a robust key management mechanism utilizing an Improved Diffie-Hellman (IDH) algorithm. This ensures secure data transfer with a focus on data integrity, authentication, and overall enhanced protection. The validation of smart grid system is analysed using MATLAB and the parameters monitored are visualized using Adafruit web application. The outcomes demonstrate that, the proposed approach consistently outperforms state-of-art existing approaches, ensuring efficient and resilient solution of secure data transfer within smart grids. The comparative analysis with existing techniques reveals that the proposed work exhibits reduced encryption, decryption and computation times along with improved throughput, packet delivery ratio and attack detection rate.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110206"},"PeriodicalIF":4.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529495","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":"Underwater image restoration via multiscale optical attenuation compensation and adaptive dark channel dehazing","authors":"Shuai Liu ,&nbsp;Peng Chen ,&nbsp;Jianyu Lan ,&nbsp;Jianru Li ,&nbsp;Zhengxiang Shen ,&nbsp;Zhanshan Wang","doi":"10.1016/j.compeleceng.2025.110228","DOIUrl":"10.1016/j.compeleceng.2025.110228","url":null,"abstract":"<div><div>Underwater images often suffer from color cast and low visibility due to inherent factors such as light absorption, scattering, and turbidity. The quality-degraded underwater images are unfavorable for underwater research and applications.To effectively deal with these quality degradation issues, this paper presents a novel restoration framework tailored specifically for underwater images, aiming to restore their natural clarity and improve their visual quality. Firstly, a multi-scale optical attenuation compensation color correction algorithm is employed to correct the color deviations of underwater images. Subsequently, an adaptive dark channel dehazing algorithm is proposed, including the global background light estimation algorithm based on multiple optical prior properties and a more sensitive segmentation transmission map estimation algorithm. Our approach integrates advanced image restoration techniques with domain-specific optimizations, ensuring robust performance across diverse underwater conditions. We comprehensively evaluate our method on a wide range of underwater image datasets, demonstrating its effectiveness in restoring color fidelity, contrast, and texture details. Furthermore, we analyze the quantitative and qualitative impacts of our framework, showcasing its advantages over existing state-of-the-art methods. Our work not only advances the field of underwater image restoration but also provides valuable insights into designing future restoration algorithms for this domain.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110228"},"PeriodicalIF":4.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527229","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 PUF-based lightweight identity authentication protocol for Internet of Vehicles","authors":"Honglei Men,&nbsp;Li Cao,&nbsp;Guoli Zheng,&nbsp;Liang Chen","doi":"10.1016/j.compeleceng.2025.110210","DOIUrl":"10.1016/j.compeleceng.2025.110210","url":null,"abstract":"<div><div>To address the efficiency issues of vehicle identity authentication schemes based on cryptographic primitives in vehicular networks, a novel lightweight identity authentication and key agreement protocol based on Physical Unclonable Functions (PUFs) is proposed. The proposed protocol authenticates identities by generating Challenge-Response Pair (CRP) data in real time, avoiding the privacy leaks and security risks associated with traditional PUF authentication, which relies on the verifier’s pre-stored CRP data. Additionally, the proposed protocol eliminates the use of complex cryptographic primitives and digital certificates in the authentication process, thereby reducing the computational and communication overhead for verifiers and the trusted authority, significantly enhancing authentication efficiency. The security analysis shows that the protocol not only protects the real identities of vehicles but also provides traceability of malicious identities, effectively defending against various security threats, including physical cloning and replay attacks. Compared to cryptographic-based identity authentication protocols, this lightweight protocol is better suited for resource-constrained and latency-sensitive vehicular network environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110210"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519979","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":"Hybrid-Network based Dynamic Wireless Power Transfer With Reduced Power Pulsation","authors":"Shibajee Nath,&nbsp;Gao Tek Lim,&nbsp;Wei Hong Lim,&nbsp;K.M. Begam,&nbsp;Anandan Shanmugam","doi":"10.1016/j.compeleceng.2025.110229","DOIUrl":"10.1016/j.compeleceng.2025.110229","url":null,"abstract":"<div><div>Dynamic wireless power transfer (DWPT) is a promising solution for extending electric vehicle range, reducing the need for large onboard batteries, and promoting sustainability. However, existing DWPT systems encounter several challenges including the large number of compensation components, power loss, pad misalignment, and receiver power fluctuations. This paper proposes a hybrid-network based DWPT system consisting of LCC-S and S-LCC networks, along with a bipolar coupling pad design, to address these challenges. The hybrid networks are connected in parallel to a common inverter and the bipolar pads are loosely placed on the track to reduce costs. A mathematical model was developed to model the system, then a misalignment tolerance tuning method was used to tune the resonant network. A 75W system was developed, and a laboratory prototype was built to validate the proposed hybrid-network based DWPT system. The system achieved approximately 70.6% efficiency, with output fluctuations less than ±10%, ±15% tolerance to lateral misalignment, and no null power when charging. The proposed system demonstrated similar performance at different receiver speeds and misalignment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110229"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527230","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":"Optimizing reliability and safety of wind turbine systems through a hybrid control technique for low-voltage ride-through capability","authors":"Nima Khosravi ,&nbsp;Masrour Dowlatabadi ,&nbsp;Adel Oubelaid ,&nbsp;Youcef Belkhier","doi":"10.1016/j.compeleceng.2025.110205","DOIUrl":"10.1016/j.compeleceng.2025.110205","url":null,"abstract":"<div><div>This study addresses a significant challenge in reliability engineering and system safety, specifically the operation of wind turbines under fault conditions. It proposes an asymmetrical fault ride-through (AFRT) control method designed for the doubly fed induction generator (DFIG) rotor-side converter (RSC) used in wind turbines. The DFIG model is analyzed in both positive and negative rotating synchronous reference frames (PR-SRF and NR-SRF), incorporating four key components to prevent overcurrent in the RSC during AFRT conditions. The proposed control method is divided into two segments: first, reducing the four components based on boundary constraints and reference value configuration; and second, determining the control characteristic ‘ k ’ through an optimization loop using the particle swarm optimization (PSO) algorithm. The effectiveness of the PSO algorithm is compared with three other optimization methods genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The dynamic performance of the proposed method is assessed under Line-to-Line (LL) and Line-to-Line-to-Ground (LLG) fault scenarios. Simulation results demonstrate that the method successfully mitigates fluctuations caused by asymmetrical faults (AFs), achieving a 7.2% higher efficiency in AFRT than similar approaches. Ultimately, this research enhances wind turbine system safety and reliability, ensuring more robust power generation during asymmetrical fault conditions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110205"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527232","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":"Complex chromatic imaging for enhanced radar face recognition","authors":"Simy M. Baby,&nbsp;E.S. Gopi","doi":"10.1016/j.compeleceng.2025.110198","DOIUrl":"10.1016/j.compeleceng.2025.110198","url":null,"abstract":"<div><div>Face recognition with millimeter-wave radar surpasses traditional cameras with better range, less intrusion, and safe material penetration using non-ionizing radiation. However, using complex-valued millimeter wave radar data for face recognition encounters challenges in extracting and representing features due to its complex nature and compatibility issues with high-performing image-based recognition systems. This paper introduces a novel approach utilizing Complex Chromatic Images (CCI) to address these challenges and enhance radar-based face recognition. Proposed Complex Chromatic Images retain both the magnitude and phase information of radar signals, providing a comprehensive representation of facial characteristics. A Complex Chromatic Image-Convolutional Neural Network (CCI-CNN) is developed to extract features from Complex Chromatic Images. Various sub-space analysis techniques are employed to tackle the high-dimensional nature of the complex-valued data. The effectiveness of the proposed approach is evaluated using various classifiers like Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN). Extensive experimental results and different evaluation metrics reveal that the proposed images approach consistently outperforms the conventional complex data images. Furthermore, when compared to existing mm-wave radar face recognition methods, our approach stands out with an impressive 99.7% accuracy. This study showcases superior recognition performance on complex-valued data, successfully addressing a large multiclass scenario with 206 distinct classes.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110198"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527231","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":"Deep learning based medical image segmentation for encryption with copyright protection through data hiding","authors":"Monu Singh ,&nbsp;Kedar Nath Singh ,&nbsp;Amrita Mohan ,&nbsp;Amit Kumar Singh ,&nbsp;Huiyu Zhou","doi":"10.1016/j.compeleceng.2025.110202","DOIUrl":"10.1016/j.compeleceng.2025.110202","url":null,"abstract":"<div><div>The prevention of medical information leakage has gained significant attention in recent times. As a result, numerous image encryption schemes are gaining prominence in protecting the privacy of original images. However, third-party users can easily compromise and access encrypted data after decryption. Therefore, it is imperative to develop encryption systems with enhanced confidentiality to address this issue. To tackle these problems, 3D-chaos-based encryption combined with copyright protection is proposed. This achieves high security at a low time cost. The method first segments the most significant information, i.e. the region of interest (ROI) part of the medical image, through the recent deep learning-based segmentation, i.e., you only look once (YOLO) version 8, for image encryption. The 3D-chaos-based encryption encodes only the ROI part, making it well-suited for secure healthcare with a low time cost. Finally, the hash of the ROI and the MAC address of the sender system is embedded into the non-region of interest (NROI) part of the image, making it effective against copyright violation, high bandwidth and storage costs. The results of extensive experiments on COVID-19 and COCO2017 datasets indicate that the method is highly secure, cost-effective and resistant to brute-force attacks. Given the advantages of encryption and data hiding, the proposed method could be an apt choice for medical data transmission and protection against any brute-force, statistical or differential attacks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110202"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519978","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":"Improved bidirectional long short-term memory network-based short-term forecasting of photovoltaic power for different seasonal types and weather factors","authors":"Ruixian Wang,&nbsp;Rui Ma,&nbsp;Linjun Zeng,&nbsp;Qin Yan,&nbsp;Archie James Johnston","doi":"10.1016/j.compeleceng.2025.110219","DOIUrl":"10.1016/j.compeleceng.2025.110219","url":null,"abstract":"<div><div>Current photovoltaic (PV) power forecasts have not rigorously investigated the intrinsic characteristics of PV data clustering associated with various seasonal weather types to explore the potential for enhanced predictive accuracy. To address this issue, a short-term prediction method that correlates seasonal weather patterns with improved bi-directional long and short-term memory network (BiLSTM) modelling is proposed. Firstly, an improved k-means clustering algorithm is employed to categorize PV data according to each season, thereby enabling an in-depth analysis of PV characteristics under distinct seasonal weather conditions. Using a variational modal decomposition (VMD) algorithm for data decomposition, the dimensionality is then reduced using a kernel principal component analysis (KPCA) and this minimizes data redundancy. An improved bidirectional long and short-term memory network (BiLSTM) model is also deployed, and this aims to comprehensively incorporate the temporal characteristics of the data. Finally, the simulation results demonstrate that the forecast accuracy of the proposed model produces improvements of up to 58.2 %, 41.3 %, and 35.4 % over the CNN, BiLSTM, and VMD-KPCA-BiLSTM models, respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110219"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527233","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}