International Journal of Intelligent Networks最新文献

{"title":"Multi-relation-pattern knowledge graph embeddings for link prediction in hyperbolic space","authors":"Longxin Lin ,&nbsp;Huaibin Qin ,&nbsp;Quan Qi ,&nbsp;Rui Gu ,&nbsp;Pengxiang Zuo ,&nbsp;Yongqiang Cheng","doi":"10.1016/j.ijin.2025.02.002","DOIUrl":"10.1016/j.ijin.2025.02.002","url":null,"abstract":"<div><div>The aim of Knowledge Graph Embedding (KGE) is to acquire low-dimensional representations of entities and relationships for the purpose of predicting new valid triples, thereby enhancing the functionality of intelligent networks that rely on accurate data representation. In recommendation systems, for example, the model can enhance personalized suggestions by better understanding user-item relationships, especially when the relationships are hierarchical, such as in the case of user preferences across different product categories. Existing KGE models mostly learn embeddings in Euclidean space, which perform well in high-dimensional settings. However, in low-dimensional scenarios, these models struggle to accurately capture the hierarchical information of relationships in knowledge graphs (KG), a limitation that can adversely affect the performance of intelligent network systems where structured knowledge is critical for decision making and operational efficiency. Recently, the MuRP model was proposed, introducing the use of hyperbolic space for KG embedding. Using the properties of hyperbolic space, where the space near the center is small and the space away from the center is large, the MuRP model achieves effective KG embedding even in low-dimensional training conditions, making it particularly suitable for dynamic environments typical of intelligent networks. Therefore, this paper proposes a method that utilizes the characteristics of hyperbolic geometry to create an embedding model in hyperbolic space, combining translation and multi-dimensional rotation geometric transformations. This model accurately represents various relationship patterns in knowledge graphs, including symmetry, asymmetry, inversion, composition, hierarchy, and multiplicity, which are essential for enabling robust interactions in intelligent network frameworks. Experimental results demonstrate that the proposed model generally outperforms Euclidean space embedding models under low-dimensional training conditions and performs comparably to other hyperbolic KGE models. In experiments using the WN18RR dataset, the Hits@10 metric improved by 0.3% compared to the baseline model, and in experiments using the FB15k-237 dataset, the Hits@3 metric improved by 0.1% compared to the baseline model, validating the reliability of the proposed model and its potential contribution to advancing intelligent network applications.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 57-64"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing a novel network anomaly detection framework using multi-serial stacked network with optimal feature selection procedures over DDOS attacks","authors":"K. Jeevan Pradeep,&nbsp;Prashanth Kumar Shukla","doi":"10.1016/j.ijin.2024.11.001","DOIUrl":"10.1016/j.ijin.2024.11.001","url":null,"abstract":"<div><div>- Distributed denial-of-service (DDoS) attacks are the major threat that disrupts the services in the computer system and networks using traffic and targeted sources. So, real-world attack detection techniques are considered an important element in executing cybersecurity tasks. The present DDoS techniques are prone to False Positive Rates (FPR) and also it didn't acquire the complicated patterns presented in the attack traffic. Internet of Things (IoT) is a complicated network with resource-constrained devices and networks that are prone to different security threats like DDoS attacks. Later, the Software Defined Networking (SDN) with IoT models is used to enhance the access control techniques and security models. DDoS attacks are considered as an important threat in the IoT networks. Hence, it is important to construct a novel network anomaly detection model with a deep learning mechanism to resolve the limitations of the existing techniques. Initially, essential data required for the validation are gathered from the IDS ISCX 2012 dataset. The optimal features are selected from input data using the Predefined-Mud Ring Algorithm (P-MRA). The optimally selected features are provided to the Multi-Serial Stacked Networks (Multi-SSN), which is the fusion of Convolutional Autoencoder (CAE), Gated Recurrent Unit (GRU), and Bayesian Learning (BL) networks. Here, the essential features for the validation are acquired from the CAE and GRU. Then, these features are stacked and given to the BL mechanism for detecting the anomalies in the network. Further, several experimental validations are performed in the developed framework over traditional network anomaly detection mechanism.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 1-13"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online and offline collaborative abnormal traffic intelligent detection system based on elastic lightweight width learning algorithm","authors":"Yu Wang,&nbsp;Hong Huang","doi":"10.1016/j.ijin.2025.04.002","DOIUrl":"10.1016/j.ijin.2025.04.002","url":null,"abstract":"<div><div>As the boost of information technology, network security issues have been increasingly prominent. Therefore, it is crucial for maintaining network security to establish an efficient abnormal traffic detection system. The study first explained the width learning algorithm, which was used as the basic framework to introduce the elastic lightweight and gated neural networks for optimization. Finally, an online abnormal traffic detection model and an offline abnormal traffic detection model were proposed. The experimental results showed that the fastest iteration of the online detection model was 190, the prediction accuracy was 96 %, the prediction error floated only between −0.01 and 0.01, and the shortest computing time was 2.012 s. The minimum iteration for the offline detection model was 200, with the abnormal flow detection error of 0.11. The lowest average absolute percentage error was 0.141 and the normalized root mean square error was 0.207. The lowest root mean square error reached 0.175, and the highest R² error was 0.884. In summary, the two proposed models have achieved significant improvements in the accuracy and efficiency of abnormal traffic detection, providing a feasible solution for network security.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 27-35"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrared spectral imaging-based image recognition for motion detection","authors":"Yong Li","doi":"10.1016/j.ijin.2025.01.001","DOIUrl":"10.1016/j.ijin.2025.01.001","url":null,"abstract":"<div><div>The current infrared imaging recognition methods are inadequate for real-time performance and accuracy for moving objects. Furthermore, they are subject to several constraints, which makes it challenging to recognize stationary and occluded objects. Experts have conducted comprehensive research on infrared imaging, including the development of contour-based infrared motion video image acquisition, the introduction of novel infrared image generation models that align with infrared imaging principles, and the formulation of innovative methods for the joint classification of spatial-spectral and hyper-spectral images. However, none of these advancements have been implemented for enhancement. In order to improve the infrared motion target detection technology, research on image recognition technology based on infrared spectral imaging, the establishment of infrared radiation characteristics model converted image, and combined with the local binary mode for motion target feature extraction, the construction of the background model, applied to the motion detection in the recognition of motion targets. The results demonstrated that the combination effect of local binary pattern feature extraction and analysis of feature vectors increased in accuracy and detection rate with the number of images. Compared to other algorithms, the research algorithm demonstrated a superior signal-to-noise ratio and gain amplitude. The unmanned aerial vehicle signal-to-noise ratio was 13.487, with a gain amplitude of 2.214, while the civil aviation aircraft signal-to-noise ratio was 6.369, with a gain amplitude of 1.792. Therefore, using infrared image feature vectors for image recognition is more effective in motion detection, providing valuable insights for improving the recognition and detection performance of infrared detection technology.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 14-26"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teacher Probability Reconstruction based knowledge distillation within intelligent network compression","authors":"Han Chen ,&nbsp;Xuyang Teng ,&nbsp;Jiajie Su ,&nbsp;Chunhao Li ,&nbsp;Chang Hu ,&nbsp;Meng Han","doi":"10.1016/j.ijin.2025.02.001","DOIUrl":"10.1016/j.ijin.2025.02.001","url":null,"abstract":"<div><div>In the optimization of intelligent network architecture, limited resources at each node, including edge computing devices, have posed challenges for deploying large models in performance-demanding scenarios. Knowledge distillation serves as a model compression method that extracts knowledge from a large-scale teacher model and transfers it to a more lightweight student model. Previous knowledge distillation methods mainly focus on the intermediate layers of the network. However, due to privacy protection regulations that limit data sharing and access as well as computational efficiency requirements in practical scenarios, feature-based distillation encounters challenges in practical applications. We start with logit-based distillation to address these issues, enabling students to learn more representative knowledge from the teacher’s output probability distribution. Due to the structural limitations of the teacher network such as insufficient depth or width, and potential issues in the training data like noise and imbalance, the output probability distribution contains many errors. Therefore, we propose a knowledge distillation method that improves the student by correcting errors in the teacher model. Nevertheless, teacher’s errors not only bring mistakes to students but also give students greater subjectivity, enabling them to break free from the limitations of the teacher. We also retain the teacher’s thinking to prevent students from becoming biased on the remaining (non-target) categories while correcting teacher errors for students. Extensive experiments demonstrate that our method achieves competitive performance on multiple benchmarks without extra parameters.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 47-56"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource optimization algorithm for 5G core network integrating NFV and SDN technologies","authors":"Chunxue Xu","doi":"10.1016/j.ijin.2025.04.001","DOIUrl":"10.1016/j.ijin.2025.04.001","url":null,"abstract":"<div><div>The growth in network demand has driven the development of new network technologies. However, traditional network architecture cannot meet the huge traffic of transportation and different business needs. To address this issue, a specific network service function chain is formed based on the network function virtualization. Dynamic resource awareness algorithms are introduced to construct an adaptive migration model based on network function virtualization. Based on the Multi-Armed Bandit (MAB) algorithm, a dynamic routing model based on MAB is constructed by using a greedy algorithm to search for random actions. When the nodes were 200 and 500, the migration costs of the adaptive migration model based on network function virtualization were 1000 and 3000, respectively. The average migration was 350 and 900 respectively, while destination nodes' average resource occupancy rates were 52 % and 58 %, respectively. When the path failure rates were 4 % and 20 %, the algorithm's safe path rates were 96.25 % and 92.75 %. For fixed and mobile nodes, the link load rate of the dynamic routing model based on the MAB algorithm was low and the load growth was relatively stable. This dynamic routing model's link delay is significantly less than the Dijkstra algorithm. These two models can maximize server resource utilization, reduce cost consumption, and achieve maximum overall benefits.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 36-46"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personal internet of things networks: An overview of 3GPP architecture, applications, key technologies, and future trends","authors":"Fariha Eusufzai, Aldrin Nippon Bobby, Farzana Shabnam, S. Sabuj","doi":"10.1016/j.ijin.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.001","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139878827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-enhanced loT and Wireless Sensor Networks for predictive analysis and maintenance in wind turbine systems","authors":"Lei Gong, Yanhui Chen","doi":"10.1016/j.ijin.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.002","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"24 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139882379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on secure Official Document Management and intelligent Information Retrieval System based on recommendation algorithm","authors":"Liang Xing","doi":"10.1016/j.ijin.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.003","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"41 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139884638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved spectrum prediction model for cognitive radio networks using hybrid deep learning technique","authors":"M.G. Sumithra ,&nbsp;M. Suriya","doi":"10.1016/j.ijin.2024.05.003","DOIUrl":"10.1016/j.ijin.2024.05.003","url":null,"abstract":"<div><p>Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTM-MLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 286-292"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000228/pdfft?md5=cdc0b0f67bdd877ac91a21ff75bc3bee&pid=1-s2.0-S2666603024000228-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}