Egyptian Informatics Journal最新文献

{"title":"Cryptocurrency-driven ransomware syndicates operating on the darknet: A focused examination of the Arab world","authors":"Kyounggon Kim,&nbsp;Seokhee Lee,&nbsp;Sundaresan Ramachandran,&nbsp;Ibrahim Alzahrani","doi":"10.1016/j.eij.2025.100665","DOIUrl":"10.1016/j.eij.2025.100665","url":null,"abstract":"<div><div>Cybercriminals are employing sophisticated techniques to illegally obtain money from victims, with ransomware, that is the most notorious malware utilized for financial gain. This paper focuses on the Arab world, a prime target region for ransomware gangs. Due to rapid economic growth and digitalization in this region, cybercriminals are increasingly targeting it. However, there is a lack of research on ransomware crime syndication in the Arab region. Data on claimed ransomware victims from 2020 to 2023 was collected from the darknet. Analysis of ransomware gangs in this area revealed significant findings. Based on three years of data collection and analysis, 20 ransomware gangs primarily operating in the Arab region were identified in 2023. Three major ransomware gangs-LockBit, ALPHV/BlackCat, and CL0P-are predominantly targeting the Arab world, with the United Arab Emirates and Saudi Arabia being major targets, along with the manufacturing industry. In addition to identifying the ransomware gangs, the tactics, techniques, and procedures (TTP) used by them were also identified. There was 17 TTPs used by ransomware gangs. This study has also developed a platform to track ransomware gangs and cryptocurrency transactions. Bitcoin’s anonymity and popularity made it the most preferred cryptocurrency by ransomware gangs. This research lays the groundwork for further studies to understand the exact trends and data related to ransomware in the Arab world.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100665"},"PeriodicalIF":5.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI supported hybrid deep learnig method for layer 2 intrusion detection","authors":"Ilhan Firat Kilincer","doi":"10.1016/j.eij.2025.100669","DOIUrl":"10.1016/j.eij.2025.100669","url":null,"abstract":"<div><div>With rapidly developing technology, digitalization environments are also expanding. Although this situation has many positive effects on daily life, the security vulnerabilities brought about by digitalization continue to be a major concern. There is a large network structure behind many applications provided to users by organizations. A substantial network infrastructure exists behind numerous applications made available to users by organisations. It is imperative that these extensive network infrastructures, which often contain sensitive data including personal, commercial, financial and security information, possess the capability to impede cyberattacks. This study proposes the creation of a Comprehensive Layer 2 − IDS (CL2-IDS) dataset for the development of IDS systems utilised in the local network structures of organisations, in conjunction with a hybrid deep learning (DL) model for the detection of attack vectors in the proposed dataset. The proposed hybrid model is obtained by using CNN (Convolutional Neural Networks) and Bi-LSTM (Bidirectional Long Short-Term Memory) models, which are widely used in areas such as image analysis and time series data. The proposed hybrid DL model achieved an accuracy of 95.28% in the classification of the CL2-IDS dataset. It is observed that the combination of these two deep learning models, which complement each other in various ways, yields successful results in the classification of the proposed CL2-IDS dataset. In the last part of the study, the effect of the features in the CL2-IDS dataset on the classification is interpreted with SHapley Additive exPlanations (SHAP), an Explainable Artificial Intelligence (XAI) method. The study, CL2-IDS dataset and hybrid DL model, combinations of CNN and Bi-LSTM algorithms, facilitates the intrusion detection and exemplifies how DL models and XAI techniques can be used to support IDS systems.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100669"},"PeriodicalIF":5.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving data transmission through optimizing blockchain sharding in cloud IoT based healthcare applications","authors":"J. Mythili ,&nbsp;R. Gopalakrishnan","doi":"10.1016/j.eij.2025.100661","DOIUrl":"10.1016/j.eij.2025.100661","url":null,"abstract":"<div><div>The rapid progress of blockchain technology is increasingly crucial in healthcare systems, where Electronic Health Records (EHRs) store vital and confidential information. However, these systems are susceptible to security risks like unauthorized access and data breaches. To tackle these issues, a decentralized, tamper-proof, and transparent healthcare network is necessary. According to this fact, the Sec-Health protocol utilizes cryptographic techniques with blockchain and InterPlanetary File System (IPFS) to safeguard EHRs. Despite advancements, managing large files, such as medical imaging data, remains a challenge due to scalability issues and limited research. Introducing sharding has the potential to improve network scalability, but if not configured correctly, it could result in orphan blocks and forks, leading to security vulnerabilities and network delays. To address this, a new sharded blockchain-based protocol called an Adaptive Sec-Health (AdaSec-Health) is proposed, utilizing an Enhanced Coati Optimization Algorithm (ECOA) in high-throughput and low-latency healthcare systems. The ECOA optimizes multiple factors to minimize orphan blocks and forks while balancing Fork Probability (FP) and User Experience (UE). Also, it introduces a cost function to optimize network security-stability tradeoffs. Thus, AdaSec-Health protocol improves the scalability and security of healthcare blockchain systems. The experiments are conducted on the network EIP-1559 using 1000 nodes with 1 to 4 shards to validate the scalability and security of the AdaSec-Health protocol. The results demonstrate that the AdaSec-Health protocol achieves 3280 transactions per second (tps) of mean throughput, 28 s of mean user-perceived latency, 0.47 gas unit of average marginal cost, 36 transactions of average block size, and a 13-second mean interval between blocks for 1000 nodes in 4 shards compared to the other healthcare blockchain systems. In terms of security analysis, AdaSec-Health achieves 7087 tps, 6738 tps, and 6400 tps for simple attacks, camouflage attacks, and observe-act attacks across 20 epochs.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100661"},"PeriodicalIF":5.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved lightweight node storage solutions in blockchain","authors":"Hongping Cao ,&nbsp;Raja Kumar Murugesan ,&nbsp;Hongxing Cao ,&nbsp;Hengfeng Shen","doi":"10.1016/j.eij.2025.100648","DOIUrl":"10.1016/j.eij.2025.100648","url":null,"abstract":"<div><div>Lightweight nodes in blockchain systems face challenges in terms of dependence, verification efficiency, and security due to their limited storage and growing data volume. This article focuses on two types of lightweight nodes: lightweight clients (e.g., Bitcoin wallets) and DHT (Distributed Hash Table) cluster nodes. Lightweight clients rely entirely on full nodes for transaction verification, resulting in dependence and vulnerability. DHT cluster nodes share storage; thereby, each node maintains a fraction of the data and retrieves the remaining data from other nodes. This will introduce processing latency when verifying new transactions. Testing conducted on Bitcoin indicates that nodes maintaining recent blocks can locally verify most new transactions. Based on this, this article proposes a new design, RBS (Recent Block Storage), where each cluster node stores recent blocks and shares older ones. Lightweight clients expand storage for recent blocks. Test results on Bitcoin show this design can reduce remote data retrieval and associated processing delays for lightweight nodes by 90 % with only 8 GB of extra storage per node. This design improves the independence and security of lightweight clients and reduces inter-node data retrieval within DHT clusters. It will facilitate the broader application of blockchain technology across various fields.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100648"},"PeriodicalIF":5.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clipper: An efficient cluster-based data pruning technique for biomedical data to increase the accuracy of machine learning model prediction","authors":"M.B. Karadeniz ,&nbsp;Ebru Efeoğlu ,&nbsp;Burak Çelik ,&nbsp;Adem Kocyigit ,&nbsp;Bahattin Türetken","doi":"10.1016/j.eij.2025.100641","DOIUrl":"10.1016/j.eij.2025.100641","url":null,"abstract":"<div><div>The exponential rise in clinical research costs can potentially be mitigated by half through the implementation of machine learning-driven efficient data processing techniques. Traditional methods like data preprocessing and hyperparameter tuning, which are effective for model optimization, often introduce complexities that can diminish the benefits of machine learning integration. To overcome this issue, we present Clipper: a novel, cluster-based data pruning approach designed specifically for biomedical data, aiming to enhance the predictive accuracy of machine learning models. Clipper’s key advantage lies in its ability to automate the data pruning process, optimizing accuracy without the need for manual hyperparameter adjustments—a typically cumbersome aspect of machine learning tasks. Upon comprehensive comparative analysis, the proposed Clipper methodology demonstrates superior performance across various medical and biological datasets. Our experiments reveal Clipper’s consistent superiority over baseline models, with significant accuracy improvements: 44% for Heart Disease, 7% for Breast Cancer, 40% for Parkinson’s, and 20% for Raisin classification. Specifically, the model achieves remarkable predictive accuracy, with classification rates of 99.5% for Heart Disease, 99.64% for Breast Cancer, 99.47% for Parkinson’s Disease, and 93% for Raisin Classification, thereby substantially outperforming contemporary state-of-the-art computational techniques. The empirical evidence suggests that Clipper serves as an effective accuracy enhancer for baseline models, eliminating the need for parameter tuning or complex preprocessing steps. Furthermore, Clipper produces robust outputs even at very low split rates, where baseline models typically perform poorly.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100641"},"PeriodicalIF":5.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A real-time system for monitoring and classification of human falls on stairs using 2.4 GHz XBee3 micro modules with a tri-axial accelerometer and KNN algorithms","authors":"Apidet Booranawong ,&nbsp;Sittiporn Sukveeraphan ,&nbsp;Liangrui Pan ,&nbsp;Nattha Jindapetch ,&nbsp;Pornchai Phukpattaranont ,&nbsp;Hiroshi Saito","doi":"10.1016/j.eij.2025.100643","DOIUrl":"10.1016/j.eij.2025.100643","url":null,"abstract":"<div><div>In this paper, a monitoring and classification system for human activities on stairs is presented. The contribution of this work is that, first, we develop the real-time wireless sensor monitoring system for measuring human motion data using 2.4 GHz IEEE 802.15.4 XBee3 micro modules as the low-power wireless modules, where the GY-521 accelerometer sensor is attached. Here, human activities on stairs, including stair ascent, stair descent, turning around, and falling, are mainly focused on preventing any dangerous accidents. Second, using the measured data, the signal vector magnitude (SVM) calculation, signal filtering using an exponentially weighted moving average (EWMA), feature extraction using the mean, maximum, interquartile range (IQR), standard deviation (STDEV), variance, and peak-to-peak (PTP) amplitude, and classification using the K-nearest neighbors (KNN) algorithm are applied. Experiments have been conducted in a home scenario. Results indicate that the proposed system can efficiently monitor human activities on stairs in real-time with reliable communications, as indicated by a strong level of the received signal strength indicator (RSSI), and a packet delivery ratio (PDR) of 100 % for both line-of-sight (LoS) and non-line-of-sight (NLoS) communications. Additionally, the proposed system using only one variance feature and the KNN classifier provides classification accuracy of 89 % for stair ascent, 70 % for stair descent, 95 % for turning around, and 100 % for falling (a critical or focused event); 88 % on average results. Thus, our system, which includes devices and classification algorithms, has the potential to monitor and categorize human falls on stairs via wireless communication, and it can be applied in practical situations.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100643"},"PeriodicalIF":5.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An innovative approach to advanced voice classification of sacred Quranic recitations through multimodal fusion","authors":"Esraa Hassan ,&nbsp;Abeer Saber ,&nbsp;Omar Alqahtani ,&nbsp;Nora El-Rashidy ,&nbsp;Samar Elbedwehy","doi":"10.1016/j.eij.2025.100640","DOIUrl":"10.1016/j.eij.2025.100640","url":null,"abstract":"<div><div>The Quran is the most important book we have ever read or recited. Perfecting recitation of the Holy Quran is challenging. In this paper, we integrate the use of multimodal fusion to result in advanced voice classification of sacred Quranic recitations. The proposed work called Voice Shortcut Connection Fusion (VSCF) architecture also targets restrictions corresponding to the dataset size and reciters’ variations into which Residual Neural Network (ResNet50) with the Fusion Layer incorporated in voice classification is integrated. The VSCF architecture is designed in a highly complex manner and is designed to be highly sophisticated about the extent to which it can approximate high-level features as well as higher-level features within a wide range of acoustic signals. The Fusion Layer proves to be an important layer that combines the ResNet50 model’s final layer with the Global Average Pooling of the raw MFCC features of the audios. This synergistic fusion enhances the ability of the model by a vast extent to identify the underlying stylistic features inherent in each reciter’s performance. The dataset consists of a Quranic Recitation Dataset having 7144 WAV format audio files from 12 Quran reciters. Compared with the traditional voice classification strategies, VSCF aims at solving issues regarding limitations of the adopted datasets and variations among different reciters. The results from our experiment showcase the effectiveness of the VSCF architecture, achieving an accuracy of 0.97683%. Further metrics include sensitivity at 0.9752, specificity at 0.9785, precision at 0.9875, and an F1 score of 0.9813.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100640"},"PeriodicalIF":5.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRFE-driven gene selection with multi-classifier ensemble for cancer classification","authors":"Smitirekha Behuria ,&nbsp;Sujata Swain ,&nbsp;Anjan Bandyopadhyay ,&nbsp;Mohammad Khalid Al-Sadoon ,&nbsp;Saurav Mallik","doi":"10.1016/j.eij.2025.100637","DOIUrl":"10.1016/j.eij.2025.100637","url":null,"abstract":"<div><div>In this era, cancer remains a paramount concern due to its pervasive impact on individuals and societies, persistent challenges in treatment and prevention, and the ongoing need for global collaboration and innovation to improve outcomes and reduce its burden. Cancer marked by uncontrolled cell growth is a leading global cause of mortality, necessitating advanced methods for accurate diagnosis. This study introduces an innovative unsupervised feature selection technique Principal Recursive Feature Eliminator (PRFE) for selection of genes and cancer classification. Subsequently, seven different classifiers—Support Vector Machine, Random Forest, CatBoost, Light Gradient Boosting Method, Artificial Neural Network, Convolutional Neural Network, Long Short-Term Memory are used to increase the model’s robustness. The proposed approach is evaluated on nine benchmark gene expression datasets with a combination of two different algorithms. A series of experiments are conducted to assess the proposed method, focusing on the selected features and identifying optimal classifiers. We have calculated F1-Score, accuracy, recall, and precision. The suggested strategy performs better than expected, as the results highlight its potential to improve cancer classification techniques with an accuracy of 99.98%. We conclude from this analysis that, across many datasets, the CatBoost and CNN model outperforms the other models. This research contributes to the ongoing efforts to improve diagnostic precision and treatment outcomes in cancer research.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100637"},"PeriodicalIF":5.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An IoT-based architecture for human resource management in construction sites using software defined networking (SDN) and game theory","authors":"Xuejie Niu","doi":"10.1016/j.eij.2025.100629","DOIUrl":"10.1016/j.eij.2025.100629","url":null,"abstract":"<div><div>Construction sites are notoriously hazardous. This paper explores how the Internet of Things (IoT) can improve worker safety and resource management in these complex environments. Traditional monitoring systems struggle with managing diverse sensor data and ensuring scalability on large construction projects. This research proposes a novel architecture that leverages Software-Defined Networking (SDN) to manage an IoT network of environmental, biometric, and surveillance sensors. This centralized control enables efficient topology control and improved network performance. Furthermore, a game-theoretic approach is employed to optimize resource allocation and routing decisions, minimizing interference and maximizing network throughput. The proposed approach achieves significant enhancements in key network metrics: delay is reduced by 59.49%, energy consumption drops by 18.31%, and packet delivery rate increases by 7.70%. This research demonstrates the potential of SDN-based IoT architectures to revolutionize safety and resource management in construction, paving the way for future advancements in on-site worker well-being and project efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100629"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IFC: Editorial","authors":"","doi":"10.1016/S1110-8665(25)00045-3","DOIUrl":"10.1016/S1110-8665(25)00045-3","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100652"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}