Journal of Network and Computer Applications最新文献

{"title":"Optimizing 5G network slicing with DRL: Balancing eMBB, URLLC, and mMTC with OMA, NOMA, and RSMA","authors":"Silvestre Malta ,&nbsp;Pedro Pinto ,&nbsp;Manuel Fernández-Veiga","doi":"10.1016/j.jnca.2024.104068","DOIUrl":"10.1016/j.jnca.2024.104068","url":null,"abstract":"<div><div>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), which demand a dynamic adaptation of network slicing to meet the diverse traffic needs. This dynamic adaptation presents both a critical challenge and a significant opportunity to improve 5G network efficiency. This paper proposes a Deep Reinforcement Learning (DRL) agent that performs dynamic resource allocation in 5G wireless network slicing according to traffic requirements of the 5G use cases within two scenarios: eMBB with URLLC and eMBB with mMTC. The DRL agent evaluates the performance of different decoding schemes such as Orthogonal Multiple Access (OMA), Non-Orthogonal Multiple Access (NOMA), and Rate Splitting Multiple Access (RSMA) and applies the best decoding scheme in these scenarios under different network conditions. The DRL agent has been tested to maximize the sum rate in scenario eMBB with URLLC and to maximize the number of successfully decoded devices in scenario eMBB with mMTC, both with different combinations of number of devices, power gains and number of allocated frequencies. The results show that the DRL agent dynamically chooses the best decoding scheme and presents an efficiency in maximizing the sum rate and the decoded devices between 84% and 100% for both scenarios evaluated.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104068"},"PeriodicalIF":7.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gwydion: Efficient auto-scaling for complex containerized applications in Kubernetes through Reinforcement Learning","authors":"José Santos ,&nbsp;Efstratios Reppas ,&nbsp;Tim Wauters ,&nbsp;Bruno Volckaert ,&nbsp;Filip De Turck","doi":"10.1016/j.jnca.2024.104067","DOIUrl":"10.1016/j.jnca.2024.104067","url":null,"abstract":"<div><div>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. Existing works do not consider microservice dependencies, which could lead to the application’s performance degradation when service demand increases. As dependencies increase, communication between microservices becomes more complex and frequent, leading to slower response times and higher resource consumption, especially during high demand. In addition, performance issues in one microservice can also trigger a ripple effect across dependent services, exacerbating the performance degradation across the entire application. This paper studies the impact of microservice inter-dependencies in auto-scaling by proposing <em>Gwydion</em>, a novel framework that enables different auto-scaling goals through Reinforcement Learning (RL) algorithms. <em>Gwydion</em> has been developed based on the OpenAI Gym library and customized for the popular Kubernetes (K8s) platform to bridge the gap between RL and auto-scaling research by training RL algorithms on real cloud environments for two opposing reward strategies: cost-aware and latency-aware. <em>Gwydion</em> focuses on improving resource usage and reducing the application’s response time by considering microservice inter-dependencies when scaling horizontally. Experiments with microservice benchmark applications, such as Redis Cluster (RC) and Online Boutique (OB), show that RL agents can reduce deployment costs and the application’s response time compared to default scaling mechanisms, achieving up to 50% lower latency while avoiding performance degradation. For RC, cost-aware algorithms can reduce the number of deployed pods (2 to 4), resulting in slightly higher latency (<span><math><mrow><mn>300</mn><mspace></mspace><mi>μ</mi><mi>s</mi></mrow></math></span> to 6 ms) but lower resource consumption. For OB, all RL algorithms exhibit a notable response time improvement by considering all microservices in the observation space, enabling the sequential triggering of actions across different deployments. This leads to nearly 30% cost savings while maintaining consistently lower latency throughout the experiment. Gwydion aims to advance auto-scaling research in a rapidly evolving dynamic cloud environment.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104067"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Handover Authenticated Key Exchange for Multi-access Edge Computing","authors":"Yuxin Xia ,&nbsp;Jie Zhang ,&nbsp;Ka Lok Man ,&nbsp;Yuji Dong","doi":"10.1016/j.jnca.2024.104071","DOIUrl":"10.1016/j.jnca.2024.104071","url":null,"abstract":"<div><div>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 to assist the authentication between them, which goes against MEC’s purpose of bringing cloud services from cloud to closer to end-user. To address the above problems, this paper proposes a new type of AKE, named as Handover Authenticated Key Exchange (HAKE). In HAKE, an earlier AKE procedure handovers authentication materials and some parameters to its temporally next AKE procedure, thereby saving resources and reducing the participation of remote cloud. Following the framework of HAKE, we propose a concrete HAKE protocol based on Elliptic Curve Diffie–Hellman (ECDH) key exchange and ratcheted key exchange. Then we verify its security via Burrows-Abadi-Needham (BAN) logic and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Finally, we evaluate and test its performance. The results show that the HAKE protocol achieves security goals and reduces communication and computation costs compared to similar protocols.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104071"},"PeriodicalIF":7.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Community Detection method based on Random walk and Multi objective Evolutionary algorithm in complex networks","authors":"Fahimeh Dabaghi-Zarandi,&nbsp;Mohammad Mehdi Afkhami,&nbsp;Mohammad Hossein Ashoori","doi":"10.1016/j.jnca.2024.104070","DOIUrl":"10.1016/j.jnca.2024.104070","url":null,"abstract":"<div><div>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 gain a better understanding of network topology and functionality. In this regard, we propose a novel community detection method in this paper that is performed based on our defined architecture composed of four components including Pre-Processing, Primary Communities Composing, Population Generating, and Genetic Mutation components. In the first component, we identify and store similarity measures and estimate the number of communities. The second component composes primary community structures based on several random walks from significant center nodes. Afterwards, our identified primary community structure is converted to a suitable chromosome structure to use in next evolutionary-based components. In the third component, we generate a primary population along with their objective function. Then, we select several significant chromosomes from the primary population and merge their communities in order to generate subsequent populations. Finally, in the fourth component, we extract several best chromosomes and apply the mutation process on them to reach the best community structure considering evaluation functions. We evaluate our proposal based on different size of network scenarios including both real and artificial network scenarios. Compared with other approaches, the community structures detected by our proposal are not dependent on the size of networks and exhibit acceptable evaluation measures in all types of networks. Therefore, our proposal can detect results similar to real community structure.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104070"},"PeriodicalIF":7.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-inspired intelligent framework for logistic theft control","authors":"Abed Alanazi ,&nbsp;Abdullah Alqahtani ,&nbsp;Shtwai Alsubai ,&nbsp;Munish Bhatia","doi":"10.1016/j.jnca.2024.104055","DOIUrl":"10.1016/j.jnca.2024.104055","url":null,"abstract":"<div><div>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 suggested model analyzes logistics transmission patterns through secure surveillance enabled by IoT-based blockchain technology. Additionally, a bi-directional convolutional neural network is employed to evaluate real-time theft vulnerabilities, aiding optimal decision-making. The proposed method has been shown to provide accurate real-time analysis of risky behaviors. Experimental simulations indicate that the proposed solution significantly improves logistics monitoring. The system’s performance is assessed using various statistical metrics, including latency rate (7.44 s), a data processing cost (<span><math><mrow><mi>O</mi><mrow><mo>(</mo><mrow><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo></mrow><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span>), and model training and testing results (precision (94.60%), recall (95.67%), and F-Measure (96.64%)), statistical performance (error reduction (48%)) and reliability (94.48%).</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104055"},"PeriodicalIF":7.7,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN","authors":"Yulong Ma ,&nbsp;Yingya Guo ,&nbsp;Ruiyu Yang ,&nbsp;Huan Luo","doi":"10.1016/j.jnca.2024.104054","DOIUrl":"10.1016/j.jnca.2024.104054","url":null,"abstract":"<div><div>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 designed. In this paper, we propose FRRL, a Reinforcement Learning (RL) approach to intelligently perceive network failures and timely compute the routing policy for improving the network performance when link failure happens. Specifically, to perceive the link failures, we design a Topology Difference Vector (TDV) encoder module in FRRL for encoding the topology structure with link failures. To efficiently compute the routing policy when link failures happen, we integrate the TDV in the agent training for learning the map between the encoded failure topology structure and routing policies. To evaluate the performance of our proposed method, we conduct experiments on three network topologies and the experimental results demonstrate that our proposed method has superior performance when link failures happen compared to other methods.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104054"},"PeriodicalIF":7.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAT-Net: A staggered attention network using graph neural networks for encrypted traffic classification","authors":"Zhiyuan Li,&nbsp;Hongyi Zhao,&nbsp;Jingyu Zhao,&nbsp;Yuqi Jiang,&nbsp;Fanliang Bu","doi":"10.1016/j.jnca.2024.104069","DOIUrl":"10.1016/j.jnca.2024.104069","url":null,"abstract":"<div><div>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 based on a Staggered Attention Network using Graph Neural Networks (SAT-Net), which takes into consideration both computer network topology and user interaction processes. Firstly, we design a Packet Byte Graph (PBG) to efficiently capture the byte features of flow and their relationships, thereby transforming the encrypted traffic classification problem into a graph classification problem. Secondly, we meticulously construct a GNN-based PBG learner, where the feature remapping layer and staggered attention layer are respectively used for feature propagation and fusion, enhancing the robustness of the model. Experiments on multiple different types of encrypted traffic datasets demonstrate that SAT-Net outperforms various advanced methods in identifying VPN traffic, Tor traffic, and malicious traffic, showing strong generalization capability.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104069"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RLL-SWE: A Robust Linked List Steganography Without Embedding for intelligence networks in smart environments","authors":"Pengbiao Zhao ,&nbsp;Yuanjian Zhou ,&nbsp;Salman Ijaz ,&nbsp;Fazlullah Khan ,&nbsp;Jingxue Chen ,&nbsp;Bandar Alshawi ,&nbsp;Zhen Qin ,&nbsp;Md Arafatur Rahman","doi":"10.1016/j.jnca.2024.104053","DOIUrl":"10.1016/j.jnca.2024.104053","url":null,"abstract":"<div><div>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 terrorism may intercept this sensitive information and use it for secret communications and illegal activities. Steganography hides information in media files and prevents information leakage and interception by criminal and terrorist networks in an intelligent environment. It is an important technology to protect data integrity and security. Traditional steganography techniques often cause detectable distortions, whereas Steganography Without Embedding (SWE) avoids direct modification of cover media, thereby minimizing detection risks. This paper introduces an innovative and robust technique called Robust Linked List (RLL)-SWE, which improves resistance to attacks compared to traditional methods. Using multiple median downsampling and gradient calculations, this method extracts stable features. It restructures them into a multi-head unidirectional linked list, ensuring accurate message retrieval and high resistance to adversarial attacks. Comprehensive analysis and simulation experiments confirm the technique’s exceptional effectiveness and steganographic capacity.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104053"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FCG-MFD: Benchmark function call graph-based dataset for malware family detection","authors":"Hassan Jalil Hadi ,&nbsp;Yue Cao ,&nbsp;Sifan Li ,&nbsp;Naveed Ahmad ,&nbsp;Mohammed Ali Alshara","doi":"10.1016/j.jnca.2024.104050","DOIUrl":"10.1016/j.jnca.2024.104050","url":null,"abstract":"<div><div>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 of malware strains. Thus, we present the FCG-MFD, a benchmark dataset with extensive Function Call Graphs (FCG) for malware family detection. This dataset guarantees resistance against emerging malware families by enabling security systems. Our dataset has two sub-datasets (FCG &amp; Metadata) (1,00,000 samples) from VirusSamples, Virusshare, VirusSign, theZoo, Vx-underground, and MalwareBazaar curated using FCGs and metadata to optimize the efficacy of ML algorithms. We suggest a new malware analysis technique using FCGs and graph embedding networks, offering a solution to the complexity of feature engineering in ML-based malware analysis. Our approach to extracting semantic features via the Natural Language Processing (NLP) method is inspired by tasks involving sentences and words, respectively, for functions and instructions. We leverage a node2vec mechanism-based graph embedding network to generate malware embedding vectors. These vectors enable automated and efficient malware analysis by combining structural and semantic features. We use two datasets (FCG &amp; Metadata) to assess FCG-MFD performance. F1-Scores of 99.14% and 99.28% are competitive with State-of-the-art (SOTA) methods.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104050"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting","authors":"Andjela Jovanovic ,&nbsp;Luka Jovanovic ,&nbsp;Miodrag Zivkovic ,&nbsp;Nebojsa Bacanin ,&nbsp;Vladimir Simic ,&nbsp;Dragan Pamucar ,&nbsp;Milos Antonijevic","doi":"10.1016/j.jnca.2024.104048","DOIUrl":"10.1016/j.jnca.2024.104048","url":null,"abstract":"<div><div>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, availability, and international policy. Also, various factors need to be considered in order to make an accurate forecast. By analyzing the current literature, a gap for improvements within this domain exists. Therefore, this work suggests and explores the potential of multi-headed long short-term memory models for gasoline price forecasting, since this issue was not tackled with multi-headed models before. Additionally, since the computational requirements for such models are relatively high, work focuses on lightweight approaches that consist of a relatively low number of neurons per layer, trained in a small number of epochs. However, as algorithm performance can be heavily dependent on appropriate hyper-parameter selections, a modified variant of the particle swarm optimization algorithm is also set forth to help in optimizing the model’s architecture and training parameters. A comparative analysis is conducted using energy data collected from multiple public sources between several contemporary optimizers. The outcomes are put through a meticulous statistical validation to ascertain the significance of the findings. The best-constructed models attained a mean square error of just 0.044025 with an R-squared of 0.911797, suggesting potential for real-world use.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"233 ","pages":"Article 104048"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}