Egyptian Informatics Journal最新文献

{"title":"The application and performance optimization of multi-controller-based load balancing algorithm in computer networks","authors":"Fengfeng Guo ,&nbsp;Ailing Ye","doi":"10.1016/j.eij.2025.100678","DOIUrl":"10.1016/j.eij.2025.100678","url":null,"abstract":"<div><div>This paper addresses the critical issue of network congestion caused by the increase in network traffic in contemporary society. The computer networks serve as the foundation for information exchange and online services, and their efficiency is essential. Traditional load-balancing algorithms face challenges in handling dynamic workloads, leading to inefficient resource utilization and extended response time. To address this problem, a novel method called Genetic-Bird Swarm Optimization (GBSO) is introduced, focusing on multi-controller-based load balancing. This method involves problem modeling, analysis, and selection processes, including the selection of switches and target controllers within the network segment. The results showed that the throughput of the proposed GBSO method was about 3800, and the load index after load balancing was 0.6, indicating that the workload distribution was balanced. The accuracy of the proposed GBSO algorithm was 92.15 %, the precision was 89 %, the recall rate was 88 %, and the F1 score was 85 %, all of which were higher than the existing Naive Bayes algorithm. This study emphasizes the importance of load balancing in optimizing computer network performance. The new algorithm proposed in this article provides a reliable solution for uniform network traffic distribution, reducing the limitations of existing methods.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100678"},"PeriodicalIF":5.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830021","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 control-driven transition strategy for enhanced multi-level threshold image segmentation optimization","authors":"Laith Abualigah ,&nbsp;Mohammad H. Almomani ,&nbsp;Saleh Ali Alomari ,&nbsp;Raed Abu Zitar ,&nbsp;Vaclav Snasel ,&nbsp;Kashif Saleem ,&nbsp;Aseel Smerat ,&nbsp;Absalom E. Ezugwu","doi":"10.1016/j.eij.2025.100646","DOIUrl":"10.1016/j.eij.2025.100646","url":null,"abstract":"<div><div>This work proposes an image segmentation approach based on a multi-threshold segmentation method and the enhanced Flood Algorithm combined with the Non-Monopolize search (named Improved IFLANO). The introduced approach, depending on IFLANO, offers much better segmentation quality for various images. Based on the existing structure, two different types of optimization techniques are added within IFLANO to enhance the update dynamics during optimization. The random strategy used in the Aquila optimization procedure enhances the performance of FLA, and a self-transition adaptation enhances the exploration ability of the image analysis. IFLANO framework is implemented for multi-level threshold image segmentation wherein the evaluation metric is Kapur’s entropy-based between-class variance. Benchmarking studies against standard test images show that IFLANO works not only faster but also yields better, more stable outcomes in image segmentations within similar time frames. IFLANO is shown to put any solution a step forward in its search for more accurate alternatives than any of the considered techniques by getting 96% improvement. We also find that the proposed method got better results in solving large medical clustering applications.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100646"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817029","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":"TFKAN: Transformer based on Kolmogorov–Arnold Networks for Intrusion Detection in IoT environment","authors":"Ibrahim A. Fares ,&nbsp;Mohamed Abd Elaziz ,&nbsp;Ahmad O. Aseeri ,&nbsp;Hamed Shawky Zied ,&nbsp;Ahmed G. Abdellatif","doi":"10.1016/j.eij.2025.100666","DOIUrl":"10.1016/j.eij.2025.100666","url":null,"abstract":"<div><div>This work proposes a novel Transformer based on the Kolmogorov–Arnold Network (TFKAN) model for Intrusion Detection Systems (IDS) in the IoT environment. The TFKAN Transformer is developed by implementing the Kolmogorov–Arnold Networks (KANs) layers instead of the Multi-Layer Perceptrons (MLP) layers. Unlike the MLPs feed-forward layer, KAN layers have no fixed weights but use learnable univariate function components, enabling a more compact representation. This means a KAN can achieve comparable performance with fewer trainable parameters than a larger MLP. The RT-IoT2022, IoT23, and CICIoT2023 datasets were used in the evaluation process. The proposed TFKAN Transformer outperforms and obtains higher accuracy scores of 99.96%, 98.43%, and 99.27% on the RT-IoT2022, IoT23, and CICIoT2023 datasets, respectively. The results indicate that the developed Transformer using KAN shows promising performance in IDS within IoT environments compared to MLP layers.Transformers based on KANs are on average 78% lighter, in parameter count, than Transformers using MLPs. This makes KANs promising to be a replacement for MLPs.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100666"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816963","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 robust and efficient algorithm for graph coloring problem based on Malatya centrality and sequent independent sets","authors":"Selman Yakut","doi":"10.1016/j.eij.2025.100676","DOIUrl":"10.1016/j.eij.2025.100676","url":null,"abstract":"<div><div>The Graph Coloring Problem (GCP) is an NP-hard problem that aims to color the vertices of a graph using the minimum number of distinct colors, ensuring that adjacent vertices do not share the same color. GCP is widely applied in real-world scenarios and graph theory problems. Despite numerous studies on solving GCP, existing methods face limitations, often performing well on specific graph types but failing to deliver efficient solutions across diverse structures. This study introduces the Malatya Sequent Independent Set Coloring Algorithm as an effective solution for GCP. The algorithm utilizes the Malatya Centrality Algorithm to compute Malatya Centrality (MC) values for graph vertices, where an MC value is defined as the sum of the ratios of a vertex’s degree to its neighbors’ degrees. The algorithm selects the vertex with the lowest MC value, adds it to an independent set, and removes it along with its neighbors and edges. This process repeats until the first sequent independent set is identified. The removed set is then excluded from the original graph, and the process continues on the remaining structure to determine additional sequent independent sets, ensuring that each set corresponds to a single color group in GCP. The algorithm was tested on social network graphs, random graphs, and benchmark datasets, supported by mathematical analyses and proofs. The results confirm that the algorithm provides efficient, polynomial-time solutions for GCP and maintains high performance across various graph types, independent of constraints.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100676"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816964","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":"Enhancing security in social computing systems through knowledge learning techniques","authors":"Anitha Rajesh ,&nbsp;Ramamurthy DineshKumar ,&nbsp;Saranya Shanmugam ,&nbsp;Jaehyuk Cho ,&nbsp;Sathishkumar Veerappampalayam Easwaramoorthy","doi":"10.1016/j.eij.2025.100675","DOIUrl":"10.1016/j.eij.2025.100675","url":null,"abstract":"<div><div>Social computing systems (SCS) integrate social behaviour with computational hardware to enable conversations among technologies and individuals. To protect the integrity of behavioural integration from illegal device communications, it is essential to ensure that security is maintained inside SCS. This study aims to provide a Coherent Authorization and Authentication Technique (CA2T) specifically designed to ensure the safety of SCS connections. CA2T uses various user credentials to approve interactions while guaranteeing that authentication is not replicated. Computed and behavioural data authentications are separated through knowledge-based learning to reduce the amount of security overheads. Credential verification that is adaptable and based on different calculation needs is the initial step in the device authorization process. After that, end-to-end authentication uses a lightweight signature based on two factors for verifying interactions between devices. The experimental findings show that when compared to the leading baseline approach, NTSC, CA2T reduces false positives by 9.48 %, computational overhead by 12.28 %, authentication time by 11.38 %, and failure rates by 11.48 %. With these improvements CA2T has emerged as much more effective than previous security frameworks for protecting SCS environments; it remains scalable, has minimal latency, and can adapt to new environments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100675"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817030","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 deep learning-orchestrated garlic routing architecture for secure telesurgery operations in healthcare 4.0","authors":"Kavit Shah ,&nbsp;Nilesh Kumar Jadav ,&nbsp;Rajesh Gupta ,&nbsp;Sucheta Gupta ,&nbsp;Sudeep Tanwar ,&nbsp;Joel J.P.C. Rodrigues ,&nbsp;Fayez Alqahtani ,&nbsp;Amr Tolba","doi":"10.1016/j.eij.2025.100662","DOIUrl":"10.1016/j.eij.2025.100662","url":null,"abstract":"<div><div>Recently, the Internet of Things (IoT) has attracted different real-time services, predominantly in the healthcare domain. One such real-time IoT-based application is telesurgery, where surgeons remotely transmit surgery instructions to a robotic arm, enabling it to conduct surgical procedures on patients. Since these surgical instructions use conventional wireless networks, they can leveraged by the attackers to manipulate them and manoeuvre the entire telesurgery application. Therefore, in this paper, we used emerging technologies, such as Artificial Intelligence (AI), garlic routing (GR) networks, and blockchain, to propose an AI- and GR-based secure data instruction architecture for telesurgery applications in the healthcare 4.0 domain. A standard sensor dataset is utilized to train different AI algorithms, such as Long Short Term Memory (LSTM) and Gated Recurrent Neural Networks (GRU), for classifying malicious and non-malicious telesurgery data. Further, the non-malicious data is forwarded to the GR network that provides an end-to-end encrypted tunnel using ElGamal and Advanced Encryption Standard (AES). ElGamal encryption encrypts the session tags for each telesurgery data relayed between surgeons and the robotic arm. The tags are stored in the immutable blockchain nodes to avoid data tampering attacks that strengthen the legitimacy of the garlic routers. Among both, the GRU outperforms with test accuracy 96.89%, precision 97.32%, recall 96.46%, F1 score 96.86%, and training loss 3%. Furthermore, the telesurgery data is transmitted via an AES-based outbound tunnel and received via an AES-based inbound tunnel, offering robust security against the security threats associated with the telesurgery application. To improve the network performance, we used essential characteristics (ultra-low latency, high speed, and high reliability) of the 5G network interface between each layer of the proposed architecture. The proposed architecture is evaluated using different evaluation metrics, such as statistical analysis (training accuracy, training loss, optimizer performance, activation function performance), data compromisation rate (0.346), network throughput (1.44 Mbps), error rate, and latency comparison.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100662"},"PeriodicalIF":5.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785258","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":"Advanced AI-driven intrusion detection for securing cloud-based industrial IoT","authors":"Saima Siraj Qureshi ,&nbsp;Jingsha He ,&nbsp;Siraj Uddin Qureshi ,&nbsp;Nafei Zhu ,&nbsp;Ahsan Wajahat ,&nbsp;Ahsan Nazir ,&nbsp;Faheem Ullah ,&nbsp;Abdul Wadud","doi":"10.1016/j.eij.2025.100644","DOIUrl":"10.1016/j.eij.2025.100644","url":null,"abstract":"<div><div>The rapid integration of smart devices with cloud services in the Industrial Internet of Things (IIoT) has exposed significant vulnerabilities in conventional security protocols, making them insufficient against sophisticated cyber threats. Despite advancements in intrusion detection systems (IDS), there remains a critical need for highly accurate, adaptive, and scalable solutions for cloud-based IIoT environments. Motivated by this necessity, we propose an advanced AI-powered IDS leveraging Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Developed using Python and the Kitsune dataset, our IDS demonstrates a remarkable detection accuracy of 98.68%, a low False Negative rate of 0.01%, and an impressive F1 score of 98.62%. Comparative analysis with other deep learning models validates the superior performance of our approach. This research contributes significantly to enhancing cybersecurity in cloud-based IIoT systems, offering a robust, scalable solution to mitigate evolving cyber threats.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100644"},"PeriodicalIF":5.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768061","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":"MANET highly efficient clustering technique based on coverage k-means algorithm","authors":"Aya Ayad Hussein ,&nbsp;Hazem Noori Abdulrazzak ,&nbsp;Ali Salem Ali","doi":"10.1016/j.eij.2025.100672","DOIUrl":"10.1016/j.eij.2025.100672","url":null,"abstract":"<div><div>Mobile ad hoc networks (MANET) are employed as an alternative access for established infrastructure in areas lacking permanent connections. MANET are dynamic and flexible, all devices can communicate. This makes them particularly useful in situations like disaster recovery, military operations, or remote areas where traditional networks are unavailable. The routing techniques are more important to managing the communication and improving the network stability, reliability, and efficiency. In this paper, a Coverage K-Means cluster-based Routing Protocol (CKRP) is proposed. The CKRP has better link connectivity, a higher route lifetime, and is more reliable with minimum transmission delay. The MANET square area is divided into multiple zones based on the zone generation proposed model based on the ratio of the maximum and minimum boundary to the node coverage. The node density and zone will used to compute the number of clusters. The k-Means algorithm will used in the cluster formulation stage. In this paper, a new Cluster Head (CH) selection model was proposed as a final stage of the CKRP model to elect the optimal node as a CH. The experiment simulated the proposed model and compared it with Ad hoc On-Demand Distance Vector Routing (AODV) and Fuzzy Logic- AODV (FL-AODV). The CKRP routing reliability has 25% improvements compared with AODV for the number of nodes exceeding 90. The proposed model has a minimum delay compared with the other algorithms.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100672"},"PeriodicalIF":5.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760090","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":"Dynamic secret sharing for enhanced cloud security: Tackling eavesdropping and threshold attacks","authors":"Sijjad Ali ,&nbsp;Shuaib Ahmed Wadho ,&nbsp;Aun Yichiet ,&nbsp;Nayem Uddin Prince ,&nbsp;Ming Lee Gan ,&nbsp;Chen Kang Lee","doi":"10.1016/j.eij.2025.100660","DOIUrl":"10.1016/j.eij.2025.100660","url":null,"abstract":"<div><div>In this paper, we propose a novel approach to enhance the security of cloud collaboration while addressing key challenges in cloud computing. The proposed scheme effectively mitigates eavesdropping and threshold attacks, significantly bolstering the security of the cloud environment. Through rigorous performance evaluation, we demonstrate that our method reduces communication overhead and improves resource efficiency compared to existing solutions. Additionally, we introduce a transformation of the dynamic context secret (DCS) into a <span><math><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>n</mi><mo>)</mo></mrow></math></span> secret sharing scheme, improving its flexibility and addressing critical security issues. This innovative approach represents a significant advancement in cloud collaboration security, offering a measurable improvement over current models. By strengthening both the security and efficiency of cloud environments, our scheme lays the groundwork for more robust and secure cloud computing frameworks.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100660"},"PeriodicalIF":5.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735195","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":"Robust zero-watermarking based on dual branch neural network for ownership authentication, auxiliary information delivery and tamper detection","authors":"Rodrigo Eduardo Arevalo-Ancona,&nbsp;Manuel Cedillo-Hernandez","doi":"10.1016/j.eij.2025.100650","DOIUrl":"10.1016/j.eij.2025.100650","url":null,"abstract":"<div><div>This paper presents a robust multitask zero-watermarking scheme for ownership authentication, auxiliary information embedding, and tamper detection using a dual-branch neural network. The proposed method generates three zero-watermarking codes stored in a three-layer structure, where each layer corresponds to a different type of watermark: a binary logo for ownership authentication, a QR code for auxiliary data, and a halftone version of the original image for tamper detection. The first and third zero-watermarking codes are generated by a logical linking between the binary logo and halftone version, respectively, with a set of neural network weights. The second zero-watermarking code is created by linking the QR code with features extracted from the dual-branch neural network. This approach ensures that the original image remains undistorted and protected at the same time. Experimental results demonstrate the robustness of the proposed method against geometric distortions, common signal processing attacks, and combined attacks, achieving bit error rates below 0.005 and normalized correlation values close to or equal to 1. Additionally, the method attained an average accuracy of 98.7 % or higher in tamper detection tasks across multiple datasets, demonstrating its versatility.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100650"},"PeriodicalIF":5.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715663","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}