International Journal of Intelligent Networks最新文献

{"title":"Erratum regarding previously published articles","authors":"","doi":"10.1016/j.ijin.2023.04.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.04.003","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Page 89"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194729","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":"An inception V3 approach for malware classification using machine learning and transfer learning","authors":"Mumtaz Ahmed ,&nbsp;Neda Afreen ,&nbsp;Muneeb Ahmed ,&nbsp;Mustafa Sameer ,&nbsp;Jameel Ahamed","doi":"10.1016/j.ijin.2022.11.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2022.11.005","url":null,"abstract":"<div><p>Malware instances have been extremely used for illegitimate purposes, and new variants of malware are observed every day. Machine learning in network security is one of the prime areas of research today because of its performance and has shown tremendous growth in the last decade. In this paper, we formulate the malware signature as a 2D image representation and leverage deep learning approaches to characterize the signature of malware contained in BIG15 dataset across nine classes. The current research compares the performance of various machine learning and deep learning technologies towards malware classification such as Logistic Regression (LR), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), transfer learning on CNN and Long Short Term Memory (LSTM). The transfer learning approach using InceptionV3 resulted in a good performance with respect to the compared models like LSTM with a classification accuracy of 98.76% on the test dataset and 99.6% on the train dataset.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 11-18"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194731","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":"Cache controlled cluster networking protocol","authors":"Priyank Sunhare ,&nbsp;Manju K. Chattopadhyay","doi":"10.1016/j.ijin.2023.07.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.07.003","url":null,"abstract":"<div><p>When technologies such as Wireless Sensor Network, Internet of Things and Cloud Computing are coupled together, real-world issues can be remarkably resolved. Wireless Sensor Network sends enormous data to a cloud server via the internet. Some applications necessitate a huge number of sensors spread across a large area. Limited battery-powered sensors send lots of data to the Base Station. To save energy and extend sensor lifespan, sensors can be networked instead of talking directly with the Base Station. Our present research work proposes an innovative Cache-Controlled Cluster Networking Protocol (CCCNP). It is a cache-supervised dynamic cluster-based sensor networking technique. In CCCNP, as the complete network is under cache control, the number of caches established in the network and their location play a very crucial role. Therefore, we first formulate the equation for the appropriate number of caches and their location. Then the caches create a network cluster and support data transmission. It stabilizes cluster formation process and decreases sensor node overhead to increase network lifetime. We simulate the CCCNP, Low Energy Adaptive Clustering Hierarchy (LEACH), Improved LEACH (I-LEACH), and LEACH with Vice-cluster Head (LEACH-VH) algorithms for two different types of networks and compare them. CCCNP outperforms other algorithms both the configurations. The dead node rate decreased by three times for each round. Even the lifespan of nodes far from the BS improved by 3.51 and 2.87 times for both network configurations, respectively. The average throughput increased by 350%, and the average lifespan increased up to 289% of the rounds.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 182-192"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194736","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":"Localization for V2X communication with noisy distance measurement","authors":"Iram Javed ,&nbsp;Xianlun Tang ,&nbsp;Muhammad Asim Saleem ,&nbsp;Ashir Javed ,&nbsp;Muhammad Azam Zia ,&nbsp;Ijaz Ali Shoukat","doi":"10.1016/j.ijin.2023.11.007","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.007","url":null,"abstract":"<div><p>Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 355-360"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000350/pdfft?md5=5129457e0eafe4524036abb863432928&pid=1-s2.0-S2666603023000350-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138570691","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":"A novel heterogenous ensemble theory for symmetric 5G cells segmentation: Intelligent RAN analytics","authors":"Jean Nestor M. Dahj ,&nbsp;Kingsley A. Ogudo ,&nbsp;Leandro Boonzaaier","doi":"10.1016/j.ijin.2023.11.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.005","url":null,"abstract":"<div><p>MNOs are investing more in 5G, rolling out sites in urban and specific rural areas. Meanwhile, it remains imperative to consistently maintain the network performance above a certain threshold for optimal user experience. Symmetric network cells characterized by parallel attributes in terms of capacity and coverage are instrumental in planning, optimization, and resource allocation. However, the variation in environmental factors introduces divergence in network cells' behavior, symmetric or not. Therefore, the need arises for intelligent analytic processes within the RAN system to categorize symmetric cells based on their performance and behavior. Intelligent optimization and analytics in 5G rely on the accurate and automated identification of cells exhibiting symmetric behavior, enabling bulk optimization operations. In this paper, we develop and assess a clustering approach using a heterogenous ensemble method to group 5G cells based on their key performance attributes to facilitate network optimization tasks. The approach involves a synergistic integration of K-means and hierarchical clustering algorithms, enabling dynamic segmentation of cells based on their performance behavior. Leveraging the clustering output, we train an XGBoost classifier, paving the way for a comprehensive analytics framework and problematic or poor-performing cells’ detection. We apply the study model to real-world 5G RAN metrics and evaluate the proposed method in terms of clustering accuracy and convergence. The study output showcases the efficacity of the heterogenous ensemble approach compared to individual clustering algorithms, providing a valuable baseline for network performance enhancement. With such a dynamic approach for analyzing 5G new radio (NR) performance, MNOs can move toward intelligent and self-aware networks, making informed decisions regarding resource allocation and coverage optimization.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 310-324"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000337/pdfft?md5=9cd8f4c75a1ea9589f7f0a9deac8040e&pid=1-s2.0-S2666603023000337-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138466571","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":"Multi-sensor based strategy learning with deep reinforcement learning for unmanned ground vehicle","authors":"Mingyu Luo","doi":"10.1016/j.ijin.2023.11.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.11.003","url":null,"abstract":"<div><p>As intelligent Unmanned Ground Vehicles (UGVs) find broader applications in areas such as transportation and logistics. The fusion of multiple sensors becomes crucial, since it not only amplifies UGV perception in dynamic scenarios but also underpins their autonomous decision-making capabilities. However, many existing methods only focus on single-sensor data, overlooking the multi-sensor data integration, thereby limiting UGV's scalability and adaptability. In this paper, we introduce the Multi-Sensor Collaborative Decision Network (MSCDN) for autonomous multi-sensor fusion policy learning designed specifically for UGVs. MSCDN is dedicated to integrate the data collected by multi-sensors in simulation environment and can be migrate to real environment. Firstly, a simulation environment mirroring real environment is created, using a framework that transfers UGV decision-making from simulated to real environment with deep reinforcement learning. Secondly, MSCDN uses a multi-sensor attention fusion network to adaptively integrate sensor data, refining UGV responses in dynamic settings. Thirdly, MSCDN's efficacy is tested on both simulated and real UGV lane-keeping tasks, showcasing its superior performance in comparative experiments. Compared to baseline methods, MSCDN reduces training steps and achieves a 35.71 % higher success rate and a 37.5 % quicker task completion time, underlining its proficient multi-sensor data fusion capability.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 325-336"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603023000313/pdfft?md5=9c8a157d9e98af6d41e1960b517981c8&pid=1-s2.0-S2666603023000313-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138549109","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":"Enhance cloud security and effectiveness using improved RSA-based RBAC with XACML technique","authors":"A. Kousalya ,&nbsp;Nam-kyun Baik","doi":"10.1016/j.ijin.2023.03.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.03.003","url":null,"abstract":"<div><p>In the current era, numerous products are accessible online from anywhere and every minute. This work used encryption with access management because authenticities, anonymity, and security over accessibility are mandatory. The proposed design introduced a significantly better encryption strategy to ensure better protection of overall resource admissions. This proposed work uses Improved RSA-based role-based access control (RBAC) with extendable access connectivity markup language (XACML) to encrypt information and maintain privileges. This approach enables storing information within the online computer using cryptographic ideas and information available via a basic admission management mechanism. To ensure the overall protection of sensitive information, the encryption method is employed that merged the conventional homogeneous encryption procedure with unstable information distribution method. This hybrid technique provides user to get advantage from retrieved information in a protected manner. The overall execution of proposed work is considerably quicker than other existing encryption methods.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 62-67"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194617","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":"Three-stage data generation algorithm for multiclass network intrusion detection with highly imbalanced dataset","authors":"Kwok Tai Chui ,&nbsp;Brij B. Gupta ,&nbsp;Priyanka Chaurasia ,&nbsp;Varsha Arya ,&nbsp;Ammar Almomani ,&nbsp;Wadee Alhalabi","doi":"10.1016/j.ijin.2023.08.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.08.001","url":null,"abstract":"<div><p>The Internet plays a crucial role in our daily routines. Ensuring cybersecurity to Internet users will provide a safe online environment. Automatic network intrusion detection (NID) using machine learning algorithms has recently received increased attention recently. The NID model is prone to bias towards the classes with more training samples due to highly imbalanced datasets across different types of attacks. The challenge in generating additional training data for minority classes is the generation of insufficient data. The study's purpose is to address this challenge, which extends the data generation ability by proposing a three-stage data generation algorithm using the synthetic minority over-sampling technique, a generative adversarial network (GAN), and a variational autoencoder. A convolutional neural network is employed to extract the representative features from the data, which were fed into a support vector machine with a customised kernel function. An ablation study evaluated the effectiveness of the three-stage data generation, feature extraction, and customised kernel. This was followed by a performance comparison between our study and existing studies. The findings revealed that the proposed NID model achieved an accuracy of 91.9%–96.2% in the four benchmark datasets. In addition, it outperformed existing methods such as GAN-based deep neural networks, conditional Wasserstein GAN-based stacked autoencoder, synthesised minority oversampling technique-based random forest, and variational autoencoder-based deep neural network, by 1.51%–28.4%.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 202-210"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194622","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":"Marketing data security and privacy protection based on federated gamma in cloud computing environment","authors":"Caixia Zhang ,&nbsp;Zijian Pan ,&nbsp;Chaofan Hou","doi":"10.1016/j.ijin.2023.09.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.09.003","url":null,"abstract":"<div><p>This study aims to address the existing data security and user privacy vulnerabilities in the “cloud” environment, which is essential for ensuring the safety and integrity of data in the era of big data and cloud computing. To achieve this purpose, we propose a novel approach that combines the logit link function with a longitudinal joint learning framework for the gamma regression model. This approach enhances the application of the model and the loss function, providing a robust solution for data security and user privacy in cloud-based systems. While cloud computing technology has greatly improved the convenience of work and life, it has also introduced significant challenges related to data security and user privacy. This study leverages semantic web technology and blockchain technology to establish a distributed and credit-guaranteed product quality and safety traceability application. By designing a concept verification system and ensuring data integrity at each stage of the product supply chain, this approach addresses these challenges effectively. The distributed network architecture employed in our technical design ensures overall system stability, reliability, and sustainability, with no single point of failure.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 261-271"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194719","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 systematic review of decentralized finance protocols","authors":"Kaushal Shah ,&nbsp;Dhruvil Lathiya ,&nbsp;Naimish Lukhi ,&nbsp;Keyur Parmar ,&nbsp;Harshal Sanghvi","doi":"10.1016/j.ijin.2023.07.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.07.002","url":null,"abstract":"<div><p>Decentralized finance (DeFi) has revolutionized the financial industry in recent years. Industries such as banking, insurance, and investment are experiencing a significant shift due to the growth of DeFi. The decentralized finance market is expanding exponentially, emphasizing the potential of digital currencies and decentralized platforms in providing an alternative to the traditional finance paradigm. It eliminates the need for costly intermediaries, reduces transaction fees, and increases accessibility to financial services for everyone, regardless of their geographic location or economic status. With the enormous increase in cryptocurrency investment, individuals and institutions have started to use DeFi. In this context, understanding the architecture and working mechanisms of different DeFi protocols becomes crucial in creating new and innovative products. This review paper explores various DeFi protocols, including derivatives, decentralized exchanges (DEX), lending and borrowing, asset management, and stablecoins. It analyses their internal structure and composability, providing insights into how these protocols can be combined to create new and innovative DeFi products and explore the potential of DeFi in providing an alternative to the traditional finance paradigm.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 171-181"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194720","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}