International Journal of Intelligent Networks最新文献

{"title":"QoE-aware edge server placement in mobile edge computing using an enhanced genetic algorithm","authors":"Jinxiang Sha ,&nbsp;Jintao Wu ,&nbsp;Mingliang Wang ,&nbsp;Yonglin Pu ,&nbsp;Sheng Lu ,&nbsp;Muhammad Bilal","doi":"10.1016/j.ijin.2025.07.003","DOIUrl":"10.1016/j.ijin.2025.07.003","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) enhances service quality by decentralizing computational resources to network edges, thereby reducing latency and improving Quality of Service (QoS). However, the spatial distribution of edge servers critically impacts network transmission efficiency, while heterogeneous user perceptions of QoS metrics frequently lead to suboptimal Quality of Experience (QoE). Current research on Edge Server Placement (ESP) predominantly focuses on localized optimization of QoS metrics, yet fails to adequately incorporate systematic QoE modeling and coordinated optimization frameworks, leading to significant discrepancies between actual user experience and satisfaction with resource allocation. To address this gap, this study establishes a formalized QoE-aware Edge Server Placement (EESP) framework by rigorously characterizing the interdependence between QoE and QoS. We first prove the NP-completeness of the EESP problem through computational complexity analysis. Subsequently, we develop an Integer Linear Programming-based exact solver (EESP-O) for small-scale scenarios and propose an Enhanced Genetic Algorithm (EESP-EGA) for large-scale deployments. The EESP-EGA integrates adaptive crossover probability mechanisms and elite retention strategies to achieve near-optimal solutions for complex real-world configurations. Experimental evaluations conducted on a broad range of real-world datasets demonstrate that the proposed method outperforms several existing representative approaches in terms of QoE.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 65-78"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694327","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":"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":"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":"A Comprehensive Survey on Emerging AI Technologies for 6G Communications: Research Direction, Trends, Challenges, and Opportunities","authors":"Barsha Rani Das ,&nbsp;Syed Rakib Hasan ,&nbsp;Saifur Rahman Sabuj ,&nbsp;Md Akbar Hossain ,&nbsp;Sayan Kumar Ray","doi":"10.1016/j.ijin.2025.06.001","DOIUrl":"10.1016/j.ijin.2025.06.001","url":null,"abstract":"<div><div>Technology is now embedded in every aspect of human existence. The demand for technological advancement is increasing day by day for seamless connections. Integrating Artificial Intelligence (AI) into technology will make the future world beyond one's imagination. The field of wireless communication is rapidly changing, and the existing wireless networks need to be improved with the growing data rate. Fifth-generation (5G) networks are now implemented throughout the world, and they meet the criteria of modern technology with higher data rates, improved quality of service, lower latency, and so forth. However, with the changing data rates after ten years, it will become impossible to meet the demand. It is expected that the upcoming sixth-generation (6G) network with the integration of AI will be the successor of 5G, and it will resolve the main issues of 5G, like higher data rates, ultra-low latency, efficiency, and so on. AI in 6G will provide a more secure, reliable communication system than its predecessor generation. This paper presents the use of AI in 6G communication networks, technologies, techniques, trends, and future research directions. It also describes in-depth knowledge of the AI-integrated 6G communication system model. Furthermore, this paper encompasses applications of AI in several technologies, challenges, and significant developments.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 113-150"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831555","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":"MIDES: A multi-layer Intrusion Detection System using ensemble machine learning","authors":"Vincenzo Agate,&nbsp;Alessandra De Paola,&nbsp;Pierluca Ferraro,&nbsp;Giuseppe Lo Re","doi":"10.1016/j.ijin.2025.09.001","DOIUrl":"10.1016/j.ijin.2025.09.001","url":null,"abstract":"<div><div>In recent years, as the frequency and types of network attacks increase, Intrusion Detection Systems (IDSs) have become essential components of most organizations’ security infrastructure. Although the use of machine learning methods shows great promise for the design of effective IDSs, existing methods still have several limitations. Single classifiers are never able to recognize all types of attacks, regardless of the underlying algorithm. This paper proposes MIDES, a novel multi-layer IDS that integrates binary, multi-class, and meta-classifiers into a flexible architecture. MIDES employs a fast binary classifier to filter clearly benign traffic, an ensemble of specialized multi-class classifiers to analyze suspicious events, and a meta-classification layer to refine decisions. A self-adaptive agent dynamically selects the most appropriate decision strategy for each input using both static and dynamic heuristics. The system is designed to be extensible, adaptable to evolving threats, and efficient in real-time scenarios. The proposed system has been extensively evaluated on the well-known CIC-IDS2017 and CSE-CIC-IDS2018 public datasets and compared against state-of-the-art works, showing that MIDES achieves high accuracy across all 14 attack classes while significantly reducing classification time, outperforming the compared systems.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 204-223"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099053","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":"Enhancing Time-Sensitive Networking resilience through SDN-based automated failover process","authors":"Asha G. Hagargund ,&nbsp;Asha K. ,&nbsp;Neelavar Shekhar Vittal Shet ,&nbsp;Muralidhar Kulkarni","doi":"10.1016/j.ijin.2025.05.001","DOIUrl":"10.1016/j.ijin.2025.05.001","url":null,"abstract":"<div><div>In domains such as industrial automation, tactile networking, and invehicle communication, stringent requirements for bounded latency and minimal packet loss are paramount to ensure the reliability and efficiency of Time-Sensitive applications. The Time-Sensitive Networking (TSN) aims to cater to these requirements. The architecture of TSN involves heterogeneous data with mixed traffic classes. To ensure the continuous availability of the TSN network, the required failover process for TSN devices must be in place. In this paper, the novel algorithm TSN Device Failover Design (TDFD) for automatic failover configuration of edge switch is proposed and validated using Linux based Open Source tools. Also, the Software Defined Networking (SDN) infrastructure is being employed to enhance the operational efficiency of distributed TSN. For the bounded latency as proposed under the IEEE 802.1Qbv, this work utilizes TAPRIO (Time Aware Priority) queuing discipline. Additionally, the impact of failover on TSN traffic is analyzed by measuring the latency. The experiment result shows that the TSN packets were sent to the destination with a delay of 6 to 13 microseconds before failover. During the failover process, there were no packet transmissions for about 160 ms due to the transition from the old TSN path to the new TSN path due to switch failure. After this, the packets are transmitted to the destination with zero loss. The time taken for the CUC to calculate the new flows and push the new flows to the backup switch is 160 ms.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 176-184"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922058","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":"Improving spam botnet detection through convolutional model and geolocation feature enhancement in a novel three-class classification task","authors":"Florentino Benedictus ,&nbsp;Muhammad Aidiel Rachman Putra ,&nbsp;Tohari Ahmad ,&nbsp;Choiru Za’in ,&nbsp;Tony de Souza-Daw","doi":"10.1016/j.ijin.2025.08.001","DOIUrl":"10.1016/j.ijin.2025.08.001","url":null,"abstract":"<div><div>Botnet detection remains a critical and challenging area in the field of information security, primarily due to the intricate architectures and sophisticated attack mechanisms employed by botnets. The significant influence of botnets on spam traffic is well-documented; however, much of the existing literature predominantly focuses on binary classification, distinguishing only between botnet and non-botnet traffic. This paper introduces a novel approach aimed at addressing this limitation by implementing an IP mapping mechanism leveraging geolocation data to enhance the quality of botnet datasets. These enriched datasets are subsequently utilized within a Convolutional Neural Network (CNN) framework to facilitate three-class classification. The proposed model differentiates among non-botnet traffic, spam botnets, and non-spam botnets, with the distinction between botnet classes driven by the substantial impact of spam botnets. The experimental results demonstrate that the proposed model achieves an average accuracy of 97.89%, along with a precision of 80.72%, recall of 72.40%, and F1-score of 73.71% across various scenarios using three distinct datasets.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 185-203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010305","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}