Egyptian Informatics Journal最新文献

{"title":"Research on network information leakage prevention method based on k-symmetric anonymous algorithm","authors":"Zaoxian Zheng,&nbsp;Hao Liu,&nbsp;Na Lin,&nbsp;Shanni Li,&nbsp;Dawei Wang","doi":"10.1016/j.eij.2025.100636","DOIUrl":"10.1016/j.eij.2025.100636","url":null,"abstract":"<div><div>In order to prevent the leakage of privacy information in published network data and improve the security of network information, a network information leakage prevention method based on K-symmetric anonymity algorithm is proposed. The relationship between individuals in the network is analyzed, and the network information graph is constructed based on graph theory to intuitively reflect the relationship between nodes in the network. The detailed query method is used to judge whether each node in the network information graph has a corresponding self-conserved equivalent node, and the simple symmetric processing is carried out to ensure that each node has a self-conserved equivalent node. Use the K-symmetric anonymous processing method to copy all nodes that have been treated with simple symmetry so that each set of equivalence classes contains more than two nodes. With the addition of network nodes, the network topology changes significantly, which reduces the probability of the attacker identifying network nodes. The improved K-symmetric anonymous algorithm only performs symmetric processing on the nodes with moderately minimal processing results. While reducing the system overhead, the network topology also presents irregular changes, making it difficult for attackers to discover the topology of the original graph, and increasing the difficulty of network information cracking. The experimental results show that this method can effectively prevent network information leakage, and has good information availability and confidentiality. By constructing a network information graph and analyzing the relationship between individuals in the network based on graph theory, the effective protection of network information is realized. It improves the security of network information and reduces the risk of disclosure of private information.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100636"},"PeriodicalIF":5.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697729","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":"Metadata association feature ATC data security assessment","authors":"Ruchun Jia ,&nbsp;Jianwei Zhang ,&nbsp;Yi Lin ,&nbsp;Yunxiang Han ,&nbsp;Yinhui Luo ,&nbsp;Fang Fei","doi":"10.1016/j.eij.2025.100667","DOIUrl":"10.1016/j.eij.2025.100667","url":null,"abstract":"<div><div>The assessment of air traffic management (ATM) security is important for maintaining the secure operation of ATM information systems. However, the accuracy of ATM assessment still needs to be improved. To solve this problem, this paper proposes a security assessment method for ATM data based on metadata correlation characteristics. The method consists of two parts: calculating the weight characteristics and optimizing the evaluation model. In the stage of calculating weight features, we extract ATM features from metadata with normalization method to obtain evaluation indicators for weight allocation. Then, the fuzzy Borda method and CRITIC method are used for weight assignment. The variable weight synthesis method is used to dynamically modify the weight, and finally the normalization method is used to achieve dimensionless processing of indicators. In the stage of optimizing the evaluation model, the multi-layer feedforward neural network is used to optimize the weights parameters. Compared with comparison methods, the accuracy of our method reaches up to 97 %, while the accuracy of compared methods fluctuates between 40 % and 80 %. In our method, the safety assessment time is up to maximum 12 s, the confidence level is always above 95 % and the p-value of the assessment results around 0.95. Comparative experimental results show that the proposed method can improve the accuracy of ATC safety assessment, and is of great significance to promote the integrity of ATM safety risk assessment system.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100667"},"PeriodicalIF":5.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696666","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":"IAPN: Framework to secure IoT-based infrastructures using Private APN","authors":"Naif Alsharabi ,&nbsp;Akashdeep Bhardwaj ,&nbsp;Talal Alshammari ,&nbsp;Shoayee alotaibi ,&nbsp;Dhahi Alshammari ,&nbsp;Amr Jadi","doi":"10.1016/j.eij.2025.100671","DOIUrl":"10.1016/j.eij.2025.100671","url":null,"abstract":"<div><div>Private access point network (APN) routes data from mobile apps and devices directly into the vendor’s corporate data networks. Thus the mobile-to-mobile IoT deployments provide options for APN, VPN, and Fixed IP. These deployments transmit data using private APNs and do not share data on the Internet. This research proposes a secure, sustainable IoT framework to implement under a real-world IoT ecosystem by using APN or Private Access Point Name. The proposed APN model focuses on delivering better visibility, security, and network traffic flow control from devices to Cloud portals. By use of Common Vulnerability Scoring System (CVSS) metrics the authors observed the presence of exploits and specific vulnerabilities in IoT environments as per the security score. Fewer vulnerabilities do not always guarantee a lower security score. The authors discuss the management and sustenance of IoT Security and architecture, vulnerability metrics, and process to measure IoT device security is also proposed by the use of Private APN. The authors performed visualization on the Kaggle dataset for IoT sensor and tolerance values, creating scatterplots and counterplots. These confirmed the values are uniform and consistent along with the distribution plot. T-test null hypothesis was calculated to validate the two independent means. The proposed design has been implemented by an Indian startup organization to monitor and secure critical infrastructure and devices in real-world scenarios.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100671"},"PeriodicalIF":5.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704522","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":"Blockchain-enhanced optimization for a secure and transparent global energy supply chain with reduced environmental impact and cost","authors":"Sivajothi Ramalingam ,&nbsp;M. Subramanian ,&nbsp;B.Srinivasa Kumar ,&nbsp;S. Rushma ,&nbsp;Nainaru Tarakaramu ,&nbsp;Aymen TRIGUI ,&nbsp;Farrukh Yuldashev ,&nbsp;Taoufik Saidani ,&nbsp;M.Ijaz Khan","doi":"10.1016/j.eij.2025.100647","DOIUrl":"10.1016/j.eij.2025.100647","url":null,"abstract":"<div><div>The rapid evolution of energy systems necessitates innovative solutions to ensure efficient, transparent, and secure energy distribution. This paper introduces a novel blockchain-based model for decentralized energy trading, aiming to optimize the global energy supply chain. The proposed model leverages smart contracts for automated peer-to-peer transactions, allowing for a significant reduction in intermediary costs and enhancing the robustness of the energy market. Through comprehensive data analysis of historical energy production figures, we establish baseline supply curves for various energy sources including coal, natural gas, nuclear, hydro, wind, solar, biofuels, and waste, from 1990 to 2021. We present a mathematical framework that encapsulates supply and demand dynamics, price adjustment mechanisms, and tokenization of energy units. The equilibrium model ensures market clearance, while the pricing algorithm dynamically responds to real-time supply and demand fluctuations. Moreover, the study formulates an optimization objective focused on maximizing social welfare, encompassing consumer and producer surplus, and minimizing environmental impacts. The model is flexible, capable of integrating future advancements in renewable energy and storage technologies. Our findings indicate that blockchain technology not only has the potential to revolutionize the way we trade energy but also to significantly contribute to a more sustainable and environmentally friendly energy landscape.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100647"},"PeriodicalIF":5.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680329","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":"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}