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A review of graph-powered data quality applications for IoT monitoring sensor networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-28 Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal
The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect of the use of sensed
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A survey on the state-of-the-art CDN architectures and future directions J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-27 Waris Ali, Chao Fang, Akmal Khan
A Content Delivery Network (CDN) consists of a distributed infrastructure of proxy servers designed to deliver digital content to end users effectively. CDNs have gained popularity due to increasing Internet users and their growing demand for low-latency content delivery. However, several unexplored aspects within CDN technology, including management, standardization, and architecture of CDNs, are
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SDN-AAA: Towards the standard management of AAA infrastructures J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-26 Francisco Lopez-Gomez, Rafa Marin-Lopez, Oscar Canovas, Gabriel Lopez-Millan, Fernando Pereniguez-Garcia
Software Defined Networking (SDN) is a widely adopted technology that enables agile and flexible management of networks and services. This paradigm is a strong candidate for addressing the dynamic and secure management of large and complex Authentication, Authorization and Accounting (AAA) infrastructures. In those infrastructures, multiple nodes must securely exchange information to interconnect different
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GridFL: A 3D-Grid-based Federated Learning framework J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-24 Jiagao Wu, Yudong Jiang, Zhouli Fan, Linfeng Liu
Federated Learning (FL) is an emerging distributed machine learning framework that enables a large number of devices to train machine learning models collaboratively without sharing local data. Despite the extensive potential of FL, in practical scenarios, different characteristics of clients lead to the presence of different heterogeneity in resources, data distribution, and data quantity, which poses
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FastTSS: Accelerating tuple space search for fast packet classification in virtual SDN switches J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-22 Bing Xiong, Jing Wu, Guanglong Hu, Jin Zhang, Baokang Zhao, Keqin Li
The increasing tendency of network virtualization gives rise to extensive deployments of virtual switches in various virtualized platforms. However, virtual switches are encountered with severe performance bottlenecks with regards to their packet classification especially in the paradigm of Software-Defined Networking (SDN). This paper is thus motivated to design a fast packet classification scheme
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Quality-aware multi-task allocation based on location importance in mobile crowdsensing J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-22 Yuping Liu, Honglong Chen, Xiang Liu, Wentao Wei, Guoqi Ma, Xiaolong Liu, Duannan Ye
Mobile crowdsensing (MCS) is a new data acquisition mode, which recruits the appropriate mobile users to complete the sensing tasks based on each task’s relevant attributes. With the budget constraints, each task can only be allocated to a limited number of users. To improve the total sensing quality, the MCS platform should employ more users for important sensing tasks. Location information is a crucial
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STARNeT: Multidimensional spatial–temporal attention recall network for accurate encrypted traffic classification J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-22 Xinjie Guan, Shuyan Zhu, Xili Wan, Yaping Wu
Network traffic classification is crucial for monitoring network health, detecting malicious activities, and ensuring Quality-of-Service (QoS). The use of dynamic ports and encryption complicates the process, rendering traditional port-based or payload-based classification methods ineffective. Conventional machine learning and statistical approaches often depend on manual feature or pattern extraction
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DRaft: A double-layer structure for Raft consensus mechanism J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-20 Jiaze Shang, Tianbo Lu, Yingjie Cai, Yanfang Li
The Raft consensus algorithm is based on the design of the leader, which simplifies the replication of logs and node changes. Unfortunately, the heavy responsibility of system interaction, including receiving requests from clients, transmitting heartbeats and entries, falls solely on the leader. A design with a strong leader can lead to an imbalance in the workload of nodes, thereby causing limited
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Internet of Things botnets: A survey on Artificial Intelligence based detection techniques J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-17 Moemedi Lefoane, Ibrahim Ghafir, Sohag Kabir, Irfan-Ullah Awan
The Internet of Things (IoT) is a game changer when it comes to digitisation across industries. The Fourth Industrial Revolution (4IR), brought about a paradigm shift indeed, unlocking possibilities and taking industries to greater heights never reached before in terms of cost saving and improved performance leading to increased productivity and profits, just to mention a few. While there are more
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Fuzzy neural network based access selection in satellite–terrestrial integrated networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-17 Weiwei Jiang, Yafeng Zhan, Xin Fang
Access selection has become a significant problem in satellite–terrestrial integrated networks (STINs) to determine the most suitable network. Existing solutions fail to solve the complexity and diversity challenges when user preferences are considered. In this study, the access selection problem in satellite–terrestrial integrated networks is considered, and user preferences for different network
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Efficient privacy-preserving ML for IoT: Cluster-based split federated learning scheme for non-IID data J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-17 Mohamad Arafeh, Mohamad Wazzeh, Hani Sami, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Hadi Otrok
In this paper, we propose a solution to address the challenges of varying client resource capabilities in the IoT environment when using the SplitFed architecture for training models without compromising user privacy. Federated Learning (FL) and Split Learning (SL) are technologies designed to maintain privacy in distributed machine learning training. While FL generally offers faster training, it requires
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A novel staged training strategy leveraging knowledge distillation and model fusion for heterogeneous federated learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-17 Debao Wang, Shaopeng Guan, Ruikang Sun
Client-side data heterogeneity poses a significant challenge in Federated Learning (FL), limiting the effectiveness of global models. To address this, we propose a staged training approach combining Knowledge Distillation and model fusion. First, a regularized KD technique trains a robust teacher model on the server, transferring knowledge to student models to enhance convergence and reduce overfitting
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Dynamically identify important nodes in the hypergraph based on the ripple diffusion and ant colony collaboration model J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-16 Peng Wang, Guang Ling, Pei Zhao, Zhi-Hong Guan, Ming-Feng Ge
Identifying important nodes plays an indispensable role in analyzing and regulating networks, and hypergraphs, as a classic high-order network, can represent the complex connections between nodes more concisely and intuitively. However, most existing methods for identifying important nodes in a hypergraph architecture are static and have low accuracy. The only few dynamic methods are very complex and
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ALB-TP: Adaptive Load Balancing based on Traffic Prediction using GRU-Attention for Software-Defined DCNs J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2025-01-04 Yong Liu, Qian Meng, Kefei Chen, Zhonghua Shen
With networks increasing in size and traffic bursting, Data Center Networks (DCNs), as the core infrastructure of High-Performance Computing (HPC), can require a high-performance, robust, and scalable load balancing method. However, existing research work has not yet met these design objectives well. In this paper, we design, analyze and evaluate a novel Adaptive Load Balancing based on Traffic Prediction
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On and off the manifold: Generation and Detection of adversarial attacks in IIoT networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-24 Mohammad Al-Fawa’reh, Jumana Abu-khalaf, Naeem Janjua, Patryk Szewczyk
Network Intrusion Detection Systems (NIDS), which play a crucial role in defending Industrial Internet of Things (IIoT) networks, often utilize Deep Neural Networks (DNN) for their pattern recognition capabilities. However, these systems remain susceptible to sophisticated adversarial attacks, particularly on-manifold and off-manifold attacks, which skillfully evade detection. This paper addresses
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Light up that Droid! On the effectiveness of static analysis features against app obfuscation for Android malware detection J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-19 Borja Molina-Coronado, Antonio Ruggia, Usue Mori, Alessio Merlo, Alexander Mendiburu, Jose Miguel-Alonso
Malware authors have seen obfuscation as the mean to bypass malware detectors based on static analysis features. For Android, several studies have confirmed that many anti-malware products are easily evaded with simple program transformations. As opposed to these works, ML detection proposals for Android leveraging static analysis features have also been proposed as obfuscation-resilient. Therefore
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Adversarial machine learning threat analysis and remediation in Open Radio Access Network (O-RAN) J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-18 Edan Habler, Ron Bitton, Dan Avraham, Eitan Klevansky, Dudu Mimran, Oleg Brodt, Heiko Lehmann, Yuval Elovici, Asaf Shabtai
O-RAN is a new, open, adaptive, and intelligent RAN architecture. Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to automatically and efficiently manage network resources in diverse use cases such as traffic steering, quality of experience prediction, and anomaly detection. Unfortunately, it has been shown that ML-based systems
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Clusters in chaos: A deep unsupervised learning paradigm for network anomaly detection J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-15 Seethalakshmi Perumal, P. Kola Sujatha, Krishnaa S., Muralitharan Krishnan
In response to the escalating sophistication of cyber threats, traditional security measures are proving insufficient, necessitating advanced solutions. The complexity of cyberattacks renders standard protocols inadequate, leading to an increased frequency of disruptions, data breaches, and financial losses. To address aforementioned challenges, a novel deep clustering algorithm developed to handle
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Consensus hybrid ensemble machine learning for intrusion detection with explainable AI J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-13 Usman Ahmed, Zheng Jiangbin, Sheharyar Khan, Muhammad Tariq Sadiq
Intrusion detection systems (IDSs) are dynamic to cybersecurity because they protect computer networks from malicious activity. IDS can benefit from machine learning; however, individual models may be unable to handle sophisticated and dynamic threats. Current cutting-edge research frequently concentrates on single machine-learning models for intrusion detection. They do not emphasize the necessity
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Adaptive differential privacy in asynchronous federated learning for aerial-aided edge computing J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-12 Yadong Zhang, Huixiang Zhang, Yi Yang, Wen Sun, Haibin Zhang, Yaru Fu
The integration of aerial-aided edge computing and federated learning (FL) is expected to completely change the way data is collected and utilized in edge computing scenarios, while effectively addressing the issues of data privacy protection and data distribution in this scenario. However, in the face of the challenge of device heterogeneity at the edge computing systems, most current synchronous
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Optimizing federated learning with weighted aggregation in aerial and space networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-11 Fan Dong, Henry Leung, Steve Drew
Federated learning offers a promising solution for overcoming the challenges of networking and data privacy in aerial and space networks by harnessing large-scale private edge data and computing resources from drones, balloons, and satellites. Although existing research has extensively explored optimizing the learning process, improving computing efficiency, and reducing communication overhead, statistical
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A survey of Machine Learning-based Physical-Layer Authentication in wireless communications J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-11 Rui Meng, Bingxuan Xu, Xiaodong Xu, Mengying Sun, Bizhu Wang, Shujun Han, Suyu Lv, Ping Zhang
To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments
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A secure routing and malicious node detection in mobile Ad hoc network using trust value evaluation with improved XGBoost mechanism J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-10 Geetika Dhand, Meena Rao, Parul Chaudhary, Kavita Sheoran
Mobile ad hoc networks (MANETs) are beneficial in a wide range of sectors because of their rapid network creation capabilities. If mobile nodes collaborate and have mutual trust, the network can function properly. Routing becomes more difficult, and vulnerabilities are exposed more quickly as a result of flexible network features and frequent relationship flaws induced by node movement. This paper
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A novel community-driven recommendation-based approach to predict and select friendships on the social IoT utilizing deep reinforcement learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-10 Babak Farhadi, Parvaneh Asghari, Ebrahim Mahdipour, Hamid Haj Seyyed Javadi
The study of how to integrate Complex Networks (CN) within the Internet of Things (IoT) ecosystem has advanced significantly because of the field's recent expansion. CNs can tackle the biggest IoT issues by providing a common conceptual framework that encompasses the IoT scope. To this end, the Social Internet of Things (SIoT) perspective is introduced. In this study, a dynamic community-driven re
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Label-aware learning to enhance unsupervised cross-domain rumor detection J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-09 Hongyan Ran, Xiaohong Li, Zhichang Zhang
Recently, massive research has achieved significant development in improving the performance of rumor detection. However, identifying rumors in an invisible domain is still an elusive challenge. To address this issue, we propose an unsupervised cross-domain rumor detection model that enhances contrastive learning and cross-attention by label-aware learning to alleviate the domain shift. The model performs
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MDQ: A QoS-Congestion Aware Deep Reinforcement Learning Approach for Multi-Path Routing in SDN J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-09 Lizeth Patricia Aguirre Sanchez, Yao Shen, Minyi Guo
The challenge of link overutilization in networking persists, prompting the development of load-balancing methods such as multi-path strategies and flow rerouting. However, traditional rule-based heuristics struggle to adapt dynamically to network changes. This leads to complex models and lengthy convergence times, unsuitable for diverse QoS demands, particularly in time-sensitive applications. Existing
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A comprehensive plane-wise review of DDoS attacks in SDN: Leveraging detection and mitigation through machine learning and deep learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-09 Dhruv Kalambe, Divyansh Sharma, Pushkar Kadam, Shivangi Surati
The traditional architecture of networks in Software Defined Networking (SDN) is divided into three distinct planes to incorporate intelligence into networks. However, this structure has also introduced security threats and challenges across these planes, including the widely recognized Distributed Denial of Service (DDoS) attack. Therefore, it is essential to predict such attacks and their variants
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Caching or re-computing: Online cost optimization for running big data tasks in IaaS clouds J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-09 Xiankun Fu, Li Pan, Shijun Liu
High computing power and large storage capacity are necessary for running big data tasks, which leads to high infrastructure costs. Infrastructure-as-a-Service (IaaS) clouds can provide configuration environments and computing resources needed for running big data tasks, while saving users from expensive software and hardware infrastructure investments. Many studies show that the cost of computation
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Intelligent energy management with IoT framework in smart cities using intelligent analysis: An application of machine learning methods for complex networks and systems J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-07 Maryam Nikpour, Parisa Behvand Yousefi, Hadi Jafarzadeh, Kasra Danesh, Roya Shomali, Saeed Asadi, Ahmad Gholizadeh Lonbar, Mohsen Ahmadi
This study addresses the growing challenges of energy consumption and the depletion of energy resources, particularly in the context of smart buildings. As the demand for energy increases alongside the need for efficient building maintenance, it becomes imperative to explore innovative energy management solutions. We present a review of Internet of Things (IoT)-based frameworks aimed at managing smart
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Complex networks for Smart environments management J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-05 Annamaria Ficara, Hocine Cherifi, Xiaoyang Liu, Luiz Fernando Bittencourt, Maria Fazio
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A survey on energy efficient medium access control for acoustic wireless communication networks in underwater environments J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-12-04 Walid K. Hasan, Iftekhar Ahmad, Daryoush Habibi, Quoc Viet Phung, Mohammad Al-Fawa'reh, Kazi Yasin Islam, Ruba Zaheer, Haitham Khaled
Underwater communication plays a crucial role in monitoring the aquatic environment on Earth. Due to their unique characteristics, underwater acoustic channels present unique challenges including lengthy signal transmission delays, limited data transfer bandwidth, variable signal quality, and fluctuating channel conditions. Furthermore, the reliance on battery power for most Underwater Wireless Acoustic
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Optimizing 5G network slicing with DRL: Balancing eMBB, URLLC, and mMTC with OMA, NOMA, and RSMA J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-28 Silvestre Malta, Pedro Pinto, Manuel Fernández-Veiga
The advent of 5th Generation (5G) networks has introduced the strategy of network slicing as a paradigm shift, enabling the provision of services with distinct Quality of Service (QoS) requirements. The 5th Generation New Radio (5G NR) standard complies with the use cases Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC)
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QuIDS: A Quantum Support Vector machine-based Intrusion Detection System for IoT networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-26 Rakesh Kumar, Mayank Swarnkar
With the increasing popularity of IoT, there has been a noticeable surge in security breaches associated with vulnerable IoT devices. To identify and counter such attacks. Intrusion Detection Systems (IDS) are deployed. However, these IoT devices use device-specific application layer protocols like MQTT and CoAP, which pose an additional burden to the traditional IDS. Several Machine Learning (ML)
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Gwydion: Efficient auto-scaling for complex containerized applications in Kubernetes through Reinforcement Learning J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-26 José Santos, Efstratios Reppas, Tim Wauters, Bruno Volckaert, Filip De Turck
Containers have reshaped application deployment and life-cycle management in recent cloud platforms. The paradigm shift from large monolithic applications to complex graphs of loosely-coupled microservices aims to increase deployment flexibility and operational efficiency. However, efficient allocation and scaling of microservice applications is challenging due to their intricate inter-dependencies
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Handover Authenticated Key Exchange for Multi-access Edge Computing J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-22 Yuxin Xia, Jie Zhang, Ka Lok Man, Yuji Dong
Authenticated Key Exchange (AKE) has been playing a significant role in ensuring communication security. However, in some Multi-access Edge Computing (MEC) scenarios where a moving end-node switchedly connects to a sequence of edge-nodes, it is costly in terms of time and computing resources to repeatedly run AKE protocols between the end-node and each edge-node. Moreover, the cloud needs to be involved
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Community Detection method based on Random walk and Multi objective Evolutionary algorithm in complex networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-22 Fahimeh Dabaghi-Zarandi, Mohammad Mehdi Afkhami, Mohammad Hossein Ashoori
In recent years, due to the existence of intricate interactions between multiple entities in complex networks, ranging from biology to social or economic networks, community detection has helped us to better understand these networks. In fact, research in community detection aims at extracting several almost separate sub-networks called communities from the complex structure of a network in order to
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Blockchain-inspired intelligent framework for logistic theft control J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-17 Abed Alanazi, Abdullah Alqahtani, Shtwai Alsubai, Munish Bhatia
The smart logistics industry utilizes advanced software and hardware technologies to enhance efficient transmission. By integrating smart components, it identifies vulnerabilities within the logistics sector, making it more susceptible to physical attacks aimed at theft and control. The main goal is to propose an effective logistics monitoring system that automates theft prevention. Specifically, the
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FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-16 Yulong Ma, Yingya Guo, Ruiyu Yang, Huan Luo
Network failures, especially link failures, happen frequently in Internet Service Provider (ISP) networks. When link failures occur, the routing policies need to be re-computed and failure recovery usually takes a few minutes, which degrades the network performance to a great extent. Therefore, a proper failure recovery scheme that can realize a fast and timely routing policy computation needs to be
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SAT-Net: A staggered attention network using graph neural networks for encrypted traffic classification J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-15 Zhiyuan Li, Hongyi Zhao, Jingyu Zhao, Yuqi Jiang, Fanliang Bu
With the increasing complexity of network protocol traffic in the modern network environment, the task of traffic classification is facing significant challenges. Existing methods lack research on the characteristics of traffic byte data and suffer from insufficient model generalization, leading to decreased classification accuracy. In response, we propose a method for encrypted traffic classification
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RLL-SWE: A Robust Linked List Steganography Without Embedding for intelligence networks in smart environments J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-14 Pengbiao Zhao, Yuanjian Zhou, Salman Ijaz, Fazlullah Khan, Jingxue Chen, Bandar Alshawi, Zhen Qin, Md Arafatur Rahman
With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and
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Heterogeneous graph representation learning via mutual information estimation for fraud detection J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-07 Zheng Zhang, Xiangyu Su, Ji Wu, Claudio J. Tessone, Hao Liao
In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labeled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and
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FCG-MFD: Benchmark function call graph-based dataset for malware family detection J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-07 Hassan Jalil Hadi, Yue Cao, Sifan Li, Naveed Ahmad, Mohammed Ali Alshara
Cyber crimes related to malware families are on the rise. This growth persists despite the prevalence of various antivirus software and approaches for malware detection and classification. Security experts have implemented Machine Learning (ML) techniques to identify these cyber-crimes. However, these approaches demand updated malware datasets for continuous improvements amid the evolving sophistication
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Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-07 Andjela Jovanovic, Luka Jovanovic, Miodrag Zivkovic, Nebojsa Bacanin, Vladimir Simic, Dragan Pamucar, Milos Antonijevic
Increasing global energy demands and decreasing stocks of fossil fuels have led to a resurgence of research into energy forecasting. Artificial intelligence, explicitly time series forecasting holds great potential to improve predictions of cost and demand with many lucrative applications across several fields. Many factors influence prices on a global scale, from socio-economic factors to distribution
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A blockchain based secure authentication technique for ensuring user privacy in edge based smart city networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-06 Abeer Iftikhar, Kashif Naseer Qureshi, Faisal Bashir Hussain, Muhammad Shiraz, Mehdi Sookhak
In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth
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Deep learning frameworks for cognitive radio networks: Review and open research challenges J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-06 Senthil Kumar Jagatheesaperumal, Ijaz Ahmad, Marko Höyhtyä, Suleman Khan, Andrei Gurtov
Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network’s capability to adapt to changing environments and improve the overall system’s
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Joint VM and container consolidation with auto-encoder based contribution extraction of decision criteria in Edge-Cloud environment J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-11-05 Farkhondeh Kiaee, Ehsan Arianyan
In the recent years, emergence huge Edge-Cloud environments faces great challenges like the ever-increasing energy demand, the extensive Internet of Things (IoT) devices adaptation, and the goals of efficiency and reliability. Containers has become increasingly popular to encapsulate various services and container migration among Edge-Cloud nodes may enable new use cases in various IoT domains. In
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Third layer blockchains are being rapidly developed: Addressing state-of-the-art paradigms and future horizons J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-28 Saeed Banaeian Far, Seyed Mojtaba Hosseini Bamakan
Undoubtedly, blockchain technology has emerged as one of the most fascinating advancements in recent decades. Its rapid development has attracted a diverse range of experts from various fields. Over the past five years, numerous blockchains have been launched, hosting a multitude of applications with varying objectives. However, a key limitation of blockchain-based services and applications is their
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Robustness of multilayer interdependent higher-order network J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-24 Hao Peng, Yifan Zhao, Dandan Zhao, Bo Zhang, Cheng Qian, Ming Zhong, Jianmin Han, Xiaoyang Liu, Wei Wang
In real-world complex systems, most networks are interconnected with other networks through interlayer dependencies, forming multilayer interdependent networks. In each system, the interactions between nodes are not limited to pairwise but also exist in a higher-order interaction composed of three or more individuals, thus inducing a multilayer interdependent higher-order network (MIHN). First, we
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PTTS: Zero-knowledge proof-based private token transfer system on Ethereum blockchain and its network flow based balance range privacy attack analysis J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-24 Goshgar Ismayilov, Can Özturan
Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analyzed. We propose a Private Token Transfer System (PTTS) for the Ethereum public
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Leveraging blockchain and federated learning in Edge-Fog-Cloud computing environments for intelligent decision-making with ECG data in IoT J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-19 Shinu M. Rajagopal, Supriya M., Rajkumar Buyya
Blockchain technology combined with Federated Learning (FL) offers a promising solution for enhancing privacy, security, and efficiency in medical IoT applications across edge, fog, and cloud computing environments. This approach enables multiple medical IoT devices at the network edge to collaboratively train a global machine learning model without sharing raw data, addressing privacy concerns associated
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Controller load optimization strategies in Software-Defined Networking: A survey J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-16 Yong Liu, Yuanhang Ge, Qian Meng, Quanze Liu
In traditional networks, the static configuration of devices increases the complexity of network management and limits the development of network functions. Software-Defined Networking (SDN) employs controllers to manage switches, thereby simplifying network management. However, with the expansion of network scale, the early single controller architecture gradually became a performance bottleneck for
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On challenges of sixth-generation (6G) wireless networks: A comprehensive survey of requirements, applications, and security issues J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-14 Muhammad Sajjad Akbar, Zawar Hussain, Muhammad Ikram, Quan Z. Sheng, Subhas Chandra Mukhopadhyay
Fifth-generation (5G) wireless networks are likely to offer high data rates, increased reliability, and low delay for mobile, personal, and local area networks. Along with the rapid growth of smart wireless sensing and communication technologies, data traffic has increased significantly and existing 5G networks are not able to fully support future massive data traffic for services, storage, and processing
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A deep reinforcement learning approach towards distributed Function as a Service (FaaS) based edge application orchestration in cloud-edge continuum J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-10 Mina Emami Khansari, Saeed Sharifian
Serverless computing has emerged as a new cloud computing model which in contrast to IoT offers unlimited and scalable access to resources. This paradigm improves resource utilization, cost, scalability and resource management specifically in terms of irregular incoming traffic. While cloud computing has been known as a reliable computing and storage solution to host IoT applications, it is not suitable
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Joint optimization scheme for task offloading and resource allocation based on MO-MFEA algorithm in intelligent transportation scenarios J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-10 Mingyang Zhao, Chengtai Liu, Sifeng Zhu
With the surge of transportation data and diversification of services, the resources for data processing in intelligent transportation systems become more limited. In order to solve this problem, this paper studies the problem of computation offloading and resource allocation adopting edge computing, NOMA communication technology and edge(content) caching technology in intelligent transportation systems
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IMUNE: A novel evolutionary algorithm for influence maximization in UAV networks J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-10 Jiaqi Chen, Shuhang Han, Donghai Tian, Changzhen Hu
In a network, influence maximization addresses identifying an optimal set of nodes to initiate influence propagation, thereby maximizing the influence spread. Current approaches for influence maximization encounter limitations in accuracy and efficiency. Furthermore, most existing methods are aimed at the IC (Independent Cascade) diffusion model, and few solutions concern dynamic networks. In this
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RT-APT: A real-time APT anomaly detection method for large-scale provenance graph J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-10 Zhengqiu Weng, Weinuo Zhang, Tiantian Zhu, Zhenhao Dou, Haofei Sun, Zhanxiang Ye, Ye Tian
Advanced Persistent Threats (APTs) are prevalent in the field of cyber attacks, where attackers employ advanced techniques to control targets and exfiltrate data without being detected by the system. Existing APT detection methods heavily rely on expert rules or specific training scenarios, resulting in the lack of both generality and reliability. Therefore, this paper proposes a novel real-time APT
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A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-10-09 Mohammed Talal, Salem Garfan, Rami Qays, Dragan Pamucar, Dursun Delen, Witold Pedrycz, Amneh Alamleh, Abdullah Alamoodi, B.B. Zaidan, Vladimir Simic
The fifth-generation (5G) network is considered a game-changing technology that promises advanced connectivity for businesses and growth opportunities. To gain a comprehensive understanding of this research domain, it is essential to scrutinize past research to investigate 5G-radio access network (RAN) architecture components and their interaction with computing tasks. This systematic literature review
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Android malware defense through a hybrid multi-modal approach J. Netw. Comput. Appl. (IF 7.7) Pub Date : 2024-09-30 Asmitha K.A., Vinod P., Rafidha Rehiman K.A., Neeraj Raveendran, Mauro Conti
The rapid proliferation of Android apps has given rise to a dark side, where increasingly sophisticated malware poses a formidable challenge for detection. To combat this evolving threat, we present an explainable hybrid multi-modal framework. This framework leverages the power of deep learning, with a novel model fusion technique, to illuminate the hidden characteristics of malicious apps. Our approach