Egyptian Informatics Journal最新文献

{"title":"Enhancing IoT security and healthcare data protection in the metaverse: A Dynamic Adaptive Security Mechanism","authors":"Saima Siraj Qureshi ,&nbsp;Jingsha He ,&nbsp;Nafei Zhu ,&nbsp;Ahsan Nazir ,&nbsp;Juan Fang ,&nbsp;Xiangjun Ma ,&nbsp;Ahsan Wajahat ,&nbsp;Faheem Ullah ,&nbsp;Sirajuddin Qureshi ,&nbsp;Sahroui Dhelim ,&nbsp;Muhammad Salman Pathan","doi":"10.1016/j.eij.2025.100670","DOIUrl":"10.1016/j.eij.2025.100670","url":null,"abstract":"<div><div>In the rapidly evolving landscape of the Metaverse, the synergistic integration of the Internet of Things (IoT) and Digital Twins (DT) represents a revolutionary paradigm shift, seamlessly bridging the real and virtual worlds. While this innovative convergence offers unprecedented potential, it also exposes a broader spectrum of security vulnerabilities that challenge conventional approaches. This research aims to fortify the multifaceted ecosystem of the Metaverse, with a particular emphasis on securing IoT healthcare data. Ensuring the protection of health information within the extensive network of interconnected devices in the Metaverse is paramount. Addressing this critical need, we introduce the Dynamic Adaptive Security Mechanism (DASM), an advanced Artificial Intelligence (AI)-driven framework meticulously crafted to enhance security adaptively. DASM operates as a comprehensive and real-time defensive layer, continuously recalibrating its strategies to reinforce the security matrix for both IoT and Digital Twins. This study provides a detailed examination of the foundational architecture of DASM and its AI-driven adaptive processes. We elucidate its pivotal role in strengthening the security framework within the complex terrain of the Metaverse. Through rigorous testing and validation using the IoT healthcare security dataset, the Random Forest model emerges as the top performer, achieving near-perfect metrics, including a Matthews Correlation Coefficient (MCC) of 0.9989 and superior Balanced Accuracy, while also offering reduced training and inference times compared to the LSTM model. Although the LSTM model demonstrates strong accuracy, the ensemble approach of Random Forest balances computational efficiency and performance. The DASM framework sets a new benchmark in IoT security, offering a scalable and effective solution with significant implications for the future of Metaverse applications.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100670"},"PeriodicalIF":5.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924300","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":"Ransomware detection and family classification using fine-tuned BERT and RoBERTa models","authors":"Amjad Hussain ,&nbsp;Ayesha Saadia ,&nbsp;Faeiz M. Alserhani","doi":"10.1016/j.eij.2025.100645","DOIUrl":"10.1016/j.eij.2025.100645","url":null,"abstract":"<div><div>Integrating Internet of Things (IoT) technologies in healthcare has revolutionized patient care, enabling real-time monitoring, predictive analytics, and personalized treatments. However, it presents significant challenges that must be addressed to ensure secure and reliable implementation. IoT devices in healthcare, such as remote patient monitors, are often constrained by limited computational power, making them vulnerable to sophisticated cyberattacks, including ransomware. In 2017 the WannaCry ransomware attack disrupted many National Health Service facilities in the United Kingdom and emphasized the critical need for robust cybersecurity measures. The lack of standardization across IoT devices creates interoperability issues and complicates data transfer between medical devices and healthcare systems. This research explores these challenges and proposes a novel approach using hyperparameter-optimized transfer learning-based models, Bidirectional Encoder Representations from Transformers (BERT), and a Robustly Optimized BERT Approach (RoBERTa), to not only detect but also classify ransomware targeting IoT devices by analyzing dynamically executed API call sequences in a sandbox environment. A total of 3300 samples from 10 ransomware families including 300 benign cases are analyzed dynamically in a sandbox environment. The newly created dataset is then preprocessed and fed to the BERT and RoBERTa models for training. The BERT achieved 95.60% accuracy with a minimal loss of 0.1650 while the RoBERTa achieved 94.39% accuracy with 0.1948 loss in classifying ransomware families. These results indicate that the proposed approach is game-changing in the classification of previously unidentified behavioral patterns inside ransomware and enhances the ability to tackle newly developing threats. By leveraging the dynamic analysis with API call sequences in a correct format, and training hyperparameter-optimized transformer learning-based models, the methodology efficiently captures behavioral patterns unique to ransomware. The research provides a scalable framework for integrating advanced detection mechanisms into real-world healthcare IoT systems, enhancing their resilience against cyber threats.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100645"},"PeriodicalIF":5.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924299","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":"LPBSA: Pre-clinical data analysis using advanced machine learning models for disease prediction","authors":"Dana R. Hamad ,&nbsp;Tarik A. Rashid","doi":"10.1016/j.eij.2025.100690","DOIUrl":"10.1016/j.eij.2025.100690","url":null,"abstract":"<div><div>Diabetes, COVID-19, and heart disease pose significant global health challenges. The current study introduces an optimization algorithm, Learner Performance-Based Behavior with Simulated Annealing (LPBSA), integrated with Multilayer Perceptron (MLP) as a neural network technique to improve disease prediction accuracy. The algorithm was tested on six preclinical datasets (one is related to diabetes, two are related to heart disease, and three are related to COVID-19). In addition to LPBSA-MLP, other optimization algorithms, including Fitness Dependent Optimizer (FDO), the original Learner Performance-Based Behavior (LPB), were independently combined with MLP. Furthermore, all three algorithms were integrated with a Cascading Multilayer Perceptron (LPBSA-CMLP, FDO-CMLP, LPB-CMLP) to enhance the convergence speed and learning capability. This allowed for a comprehensive comparison across diverse algorithmic configurations and enabled the identification of the most efficient model for disease prediction. The proposed LPBSA-MLP model achieved 100% accuracy on four data sets and at least 99.31% on the others. Further metrics confirm its performance: sensitivity and specificity values reached 100%, and Mean Square Error (MSE) values ranged from 0.0008 to 0.003. When benchmarked against models trained with FDO-MLP, LPB-MLP, and other standard optimizers, LPBSA-MLP consistently outperformed them in terms of both classification performance and convergence speed. These findings indicate the effectiveness of LPBSA in enhancing predictive modeling for critical health conditions.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100690"},"PeriodicalIF":5.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907507","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":"Towards lightweight model using non-local-based graph convolution neural network for SQL injection detection","authors":"Duc-Chinh Nguyen ,&nbsp;Manh-Hung Ha ,&nbsp;Manh-Tuan Do ,&nbsp;Oscal Tzyh-Chiang Chen","doi":"10.1016/j.eij.2025.100684","DOIUrl":"10.1016/j.eij.2025.100684","url":null,"abstract":"<div><div>SQL injection poses serious threats to web applications and databases by enabling unauthorized access and data leakage. To address this issue, we propose a unique graph network, an innovative topology not explored previously for SQL injection detection. SQL statements are nodes, and their connections form edges in the graph. We introduce three graph CNN models, including a graph classification model with a two-layer Graph Convolutional Network (GCN), a graph classification model leveraging a non-local graph convolution derived from a 1x1 convolution, supplanting the original 1x1 convolution, and a modified non-local-block module by substituting the 1x1 convolution layers in the non-local architecture with GCN. The proposed models exhibit accuracy above 99% and inference times under 1 ms on two datasets. In comparison with traditional 22 models, our models using GCN demonstrate superior computation efficiency, parameter reduction, accuracy enhancement, and the advantage of handling input sequences of any length, underlining their potential in real-world cybersecurity systems, especially in effective SQL injection detection and mitigation strategies.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100684"},"PeriodicalIF":5.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892375","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":"Intrusion detection algorithm of wireless network based on network traffic anomaly analysis","authors":"Xiangqian Nie,&nbsp;Jiao Xing,&nbsp;Qimeng Li,&nbsp;Fan Xiao","doi":"10.1016/j.eij.2025.100689","DOIUrl":"10.1016/j.eij.2025.100689","url":null,"abstract":"<div><div>Due to the openness and sharing nature of wireless networks, they are vulnerable to various network attacks. To promptly identify and mitigate abnormal behaviors while ensuring normal operation and security, this paper proposes an algorithm for detecting compromised nodes in wireless networks based on network traffic anomaly analysis. In the proposed detection architecture, a network traffic data acquisition module mines and reconstructs real-time traffic data from wireless nodes, removing redundant information. The processed data is then fed into an anomaly analysis module, where abnormal traffic features are extracted and dimensionality-reduced via a stacked autoencoder to form standardized anomaly profiles. These features are analyzed by an intrusion detection module combining particle swarm optimization and support vector machine algorithms. Experimental results demonstrate that the algorithm efficiently extracts traffic anomalies, accurately detects attack duration and traffic volume changes in compromised nodes, and maintains a false detection rate below 6 %.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100689"},"PeriodicalIF":5.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881718","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":"Design and hardware implementation of LED block cipher for vehicles keyless entry systems","authors":"Ayoub Mhaouch ,&nbsp;Wafa Gtifa ,&nbsp;Abdesslem Abdeali ,&nbsp;Anis Sakly ,&nbsp;Mohsen Machhout","doi":"10.1016/j.eij.2025.100687","DOIUrl":"10.1016/j.eij.2025.100687","url":null,"abstract":"<div><div>Security is paramount in vehicle keyless entry systems, as they are increasingly targeted by various attacks, including relay attacks, theft, and espionage. Keyless entry systems are particularly vulnerable to relay attacks, where attackers intercept and amplify the signal from the key fob, granting unauthorized access to the vehicle. This compromises the integrity of the system, emphasizing the need for robust encryption mechanisms to prevent unauthorized access and safeguard sensitive vehicle data. In this work, we propose an optimized hardware design for the Light Encryption Device (LED) cipher, aimed at enhancing both the security and efficiency of keyless entry systems. The proposed security system is evaluated using security metrics such as NPCR, UACI, entropy, and correlation analysis, demonstrating its robust protection against potential attacks. The obtained results show that the proposed hardware implementation delivers higher efficiency and enhanced security compared to existing designs, making it a promising solution for securing keyless vehicle entry systems. The real-world test scenarios assess the performance of the proposed hardware system, demonstrating its effectiveness in terms of execution time, power consumption, and battery drain time across different platforms, including Dual Core ARM Cortex-A9 and Zynq XC7Z020. The results reveal that the proposed designs offer improved efficiency and security, positioning them as a viable solution for safeguarding keyless entry systems against unauthorized access. This work underscores the potential of lightweight cryptography to tackle both security and performance challenges in modern automotive systems, ensuring the safety and integrity of vehicle access control.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100687"},"PeriodicalIF":5.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886873","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":"Climate-resilient water management: Leveraging IoT and AI for sustainable agriculture","authors":"Syed Muhammad Zaigham Abbas Naqvi ,&nbsp;Saddam Hussain ,&nbsp;Muhammad Awais ,&nbsp;Muhammad Naveed Tahir ,&nbsp;Shoaib Rashid Saleem ,&nbsp;Fuad A.M. Al-Yarimi ,&nbsp;Mirjalol Ashurov ,&nbsp;Oumaima Saidani ,&nbsp;M.Ijaz Khan ,&nbsp;Junfeng Wu ,&nbsp;Zhang Wei ,&nbsp;Jiandong Hu","doi":"10.1016/j.eij.2025.100691","DOIUrl":"10.1016/j.eij.2025.100691","url":null,"abstract":"<div><div>Climate change is the phenomenon of permanent change in the environmental conditions of an area. However, it is now affecting the earth by causing a permanent seasonal shift. This seasonal shift is not only decreasing the yields of crops by shortening their growth duration but also critically affecting the water availability for irrigation purposes. This article addresses the irrigation management strategies to mitigate the impacts of climate changes using advance techniques like internet of things (IoT). IoT is the setup of smart sensory devices which are interconnected using internet. They collect the data from field and analyze using artificial intelligence based algorithmic models. The irrigation management strategies using the artificial intelligence (AI) to mitigate the climate change impacts by reducing the wastage of essential resources in the environment has not been adopted by many developed countries. This article briefly explained the applications of AI in smart agriculture. Manuscript further describes the idea to protect the agricultural system from water scarcity and flooding by the efficient use of sensors, IoT and AI by automating the traditional agricultural practices. Different variable rate applications, smart irrigation methods like weather-based smart irrigation and moisture-based smart irrigation have been discussed in this review. Different countries have adapted different technologies of smart irrigation which can mitigate climate changes effectively and a case study with this respect is discussed. Moreover, implementations of integrated neural network models with the decision support system of irrigation management strategies to decide the supply of water in the field in real-time have been discussed in this review.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100691"},"PeriodicalIF":5.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876612","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 signal algorithm detection in NOMA 5G waveform: Transforming smart healthcare connectivity by reducing latency","authors":"Arun Kumar ,&nbsp;Nishant Gaur ,&nbsp;Aziz Nanthaamornphong","doi":"10.1016/j.eij.2025.100677","DOIUrl":"10.1016/j.eij.2025.100677","url":null,"abstract":"<div><div>The detection of hybrid signal algorithms in Non-Orthogonal Multiple Access (NOMA) 5G waveforms is changing the face of smart healthcare. The integration of NOMA allows multiple simultaneous connections in a given system, which significantly enhances spectral efficiency, ensuring unmatched communication between different medical devices and monitoring systems. Interference mitigation is guaranteed by the proper employment of hybrid signal algorithms that improve correct data interpretation, and are important for maintaining robust connectivity among healthcare facilities with heavy demands. These developments have overcome some of the key challenges in the domain of smart healthcare such as real-time data transmission for remote monitoring, telemedicine, and emergency response. Lowering latency and improving signal reliability will support rapid decision making and patient safety in critical situations. In this paper, we propose a hybrid signal detection algorithm that combines a zero-forcing equalizer (ZFE) and minimum mean square error (MMSE) for the NOMA-MIMO structure with Rician and Rayleigh channels, highlighting its role in empowering next-generation healthcare solutions through enhanced connectivity, reliability, and efficiency. For 16 × 16 and 64 × 64 MIMO-NOMA, the Bit error rate (BER) was evaluated and compared for Long Short-Term Memory (LSTM), ZFE, MMSE, and Maximum likelihood (ML) detection, and the proposed ZFE-MMSE algorithms. The simulation results revealed that the projected LSTM obtains a better BER at a low SNR with high complexity. However, ZFE-MMSE effectively detects the signal at a low SNR, outperforming contemporary algorithms at complexity similar to MMSE and ZFE, and can enhance the latency performance for smart health care applications.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100677"},"PeriodicalIF":5.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870578","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 hybrid fuzzy logic and federated learning framework for enhancing cybersecurity and fraud detection in IoT-enabled metaverse transactions","authors":"Amjad Rehman ,&nbsp;Kamran Ahmad Awan ,&nbsp;Amal Al-Rasheed ,&nbsp;Anees Ara ,&nbsp;Fahad F. Alruwaili ,&nbsp;Shaha Al-Otaibi ,&nbsp;Tanzila Saba","doi":"10.1016/j.eij.2025.100668","DOIUrl":"10.1016/j.eij.2025.100668","url":null,"abstract":"<div><div>Increasing integration of the Internet of Things (IoT) with virtual environments like the Metaverse has opened up new avenues in the applicability of technologies but faces severe challenges to security and fraud detection. Most of the existing frameworks are incapable of efficiently managing trust and detecting fraudulent activities in a decentralized, resource-constrained environment. In this article, a novel framework of cybersecurity is proposed that integrates hybrid fuzzy logic-based Trust Management with a decentralized model of Federated Learning. The proposed approach assesses and manages at runtime to maintain the degree of trust using fuzzy logic in dynamic conditions of the Metaverse. The optimized federated learning model for IoT devices implements lightweight algorithms with hierarchical aggregation that reduce computational and communication overhead to enhance fraud detection capabilities. The performance evaluation is conducted on different attack scenarios like <span><math><mrow><mi>O</mi><msub><mrow><mi>n</mi></mrow><mrow><mtext>off</mtext></mrow></msub></mrow></math></span>, Whitewashing, DDOS, and Bad Mouthing attacks. It is observed that the proposed approach performs better in comparison with existing approaches by achieving a 0.93 trust score value in low-network scenarios. It reduces computational energy consumption by 25%, thus proving the effectiveness and strength of the framework in fraud detection within IoT-enabled Metaverse environments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100668"},"PeriodicalIF":5.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870576","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":"Zero trust anonymous access algorithm for multi cloud storage system based on CP-ABE","authors":"Jun Tian","doi":"10.1016/j.eij.2025.100681","DOIUrl":"10.1016/j.eij.2025.100681","url":null,"abstract":"<div><div>This paper proposes a zero-trust anonymous access algorithm for multi-cloud storage systems based on CP-ABE (Ciphertext-Policy Attribute-Based Encryption). To address the challenges of inefficient data encryption/decryption and high communication overhead in existing systems, we design a novel access control model that integrates hierarchical identity-based encryption with enhanced CP-ABE. The model features: (1) a hierarchical identity management module for standardized authentication, (2) server-side data encryption enabling fine-grained access control, and (3) an improved CP-ABE scheme with key versioning for efficient revocation. Experimental results demonstrate that the proposed algorithm significantly improves encryption/decryption efficiency while reducing storage overhead and enhancing data-sharing security compared to conventional approaches.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100681"},"PeriodicalIF":5.0,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850489","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}