Computer Networks最新文献

{"title":"Towards the future of bot detection: A comprehensive taxonomical review and challenges on Twitter/X","authors":"","doi":"10.1016/j.comnet.2024.110808","DOIUrl":"10.1016/j.comnet.2024.110808","url":null,"abstract":"<div><div>Harmful Twitter Bots (HTBs) are widespread and adaptable to a wide range of social network platforms. The use of social network bots on numerous social network platforms is increasing. As the popularity and utility of social networking bots grow, the attacks using social network-based automated accounts are getting more coordinated, resulting in crimes that might endanger democracy, the financial market, and public health. HTB designers develop their bots to elude detection while academics create several algorithms to identify social media bot accounts. This field is active and necessitates ongoing improvement due to the never-ending cat-and-mouse game. X, previously known as Twitter, is among the biggest social network platforms that has been plagued by automated accounts. Even though new research is being conducted to tackle this issue, the number of bots on Twitter keeps on increasing. In this research, we establish a robust theoretical foundation in the continuously evolving domain of Harmful Twitter Bot (HTB) detection by analyzing the existing HTB detection techniques. Our research provides an extensive literature review and introduces an enhanced taxonomy that has the potential to help the scientific community form better generalizations for HTB detection. Furthermore, we discuss this domain's obstacles and open challenges to direct and improve future research. As far as we are aware, this study marks the first comprehensive examination of HTB detection that includes articles published between June 2013 and August 2023. The review's findings include a more thorough classification of detection approaches, a spotlight on ways to spot Twitter bots, and a comparison of recent HTB detection methods. Moreover, we provide a comprehensive list of publicly available datasets for HTB detection. As bots evolve, efforts must be made to raise awareness, equip legitimate users with information, and help future researchers in the field of social network bot detection.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327851","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":"A two-step linear programming approach for repeater placement in large-scale quantum networks","authors":"","doi":"10.1016/j.comnet.2024.110795","DOIUrl":"10.1016/j.comnet.2024.110795","url":null,"abstract":"<div><p>Thanks to the applications such as Quantum Key Distribution and Distributed Quantum Computing, the deployment of quantum networks is gaining great momentum. A major component in quantum networks is repeaters, which are essential for reducing the error rate of qubit transmission for long-distance links. However, repeaters are expensive devices, so minimizing the number of repeaters placed in a quantum network while satisfying performance requirements becomes an important problem. Existing solutions typically solve this problem optimally by formulating an Integer Linear Program (ILP). However, the number of variables in their ILPs is <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span>, where <span><math><mi>n</mi></math></span> is the number of nodes in a network. This incurs infeasible running time when the network scale is large. To overcome this drawback, this paper proposes to solve the repeater placement problem by two steps, with each step using a linear program of a much smaller scale with <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> variables. Although this solution is not optimal, it dramatically reduces the time complexity, making it practical for large-scale networks. Moreover, it constructs networks that have higher node connectivity than those by existing solutions, since it deploys slightly more number of repeaters into networks. Our extensive experiments on both synthetic and real-world network topologies verified our claims.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389128624006273/pdfft?md5=514277d17e6855a7161cf9235a13a0a6&pid=1-s2.0-S1389128624006273-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274083","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":"Network traffic prediction based on PSO-LightGBM-TM","authors":"","doi":"10.1016/j.comnet.2024.110810","DOIUrl":"10.1016/j.comnet.2024.110810","url":null,"abstract":"<div><p>Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274077","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":"GCP: A multi-strategy improved wireless sensor network model for environmental monitoring","authors":"","doi":"10.1016/j.comnet.2024.110807","DOIUrl":"10.1016/j.comnet.2024.110807","url":null,"abstract":"<div><p>Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274078","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":"A truthful double auction mechanism for resource provisioning and elastic service in vehicle computing","authors":"","doi":"10.1016/j.comnet.2024.110806","DOIUrl":"10.1016/j.comnet.2024.110806","url":null,"abstract":"<div><p>Intelligent vehicles, equipped with powerful computing and sensing resources, serve as versatile mobile computing platforms, offering many resources to users. This study focuses on resource provisioning and elastic service to address the paramount issue of resource provisioning for in-vehicle computing. It introduces an elastic sensing service, enabling users to declare multiple requested areas to obtain sensing data. It allows various vehicles to collaborate in providing services to a single user when individual vehicles cannot complete the task alone. The approach formulated as a double auction-based setting involves a market with multiple self-interested users and vehicles. The main objective is to design a mechanism that maximizes social welfare. First, a greedy mechanism provides different task allocation strategies while ensuring truthfulness. The proposed mechanism is truthful and equilibrium-driven, achieving individual rationality, consumer sovereignty, and budget balance. It demonstrates the approximation ratio. Simulation results indicate that the proposed mechanism can increase social welfare and the number of served users by at least 29% and 9%, respectively, compared with baseline methods. This research paves the way for more efficient resource provisioning in intelligent vehicles, ultimately enhancing these mobile computing platforms’ overall user experience and capabilities.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274079","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":"Multi-objective joint optimization of task offloading based on MADRL in internet of things assisted by satellite networks","authors":"","doi":"10.1016/j.comnet.2024.110801","DOIUrl":"10.1016/j.comnet.2024.110801","url":null,"abstract":"<div><div>The Internet of Things (IoT) integrates a large number of heterogeneous terminals and systems, possessing ubiquitous sensing and computing capabilities. Satellite networks are the crucial supplement to terrestrial networks, particularly in remote areas where network infrastructures are sparingly distributed or unavailable. Combining edge computing with satellite networks provides on-orbit computing capabilities for IoT applications, reducing service delay and enhancing service quality. Due to the resource constraints of satellites, achieving collaborative services through task offloading among multiple satellites becomes essential. Both the privacy leakage risk arising from frequent data interactions and the load imbalance resulting from offloading preferences cannot be overlooked. The key challenge of task offloading is to safeguard the privacy of offloaded data and ensure the system’s load balance while minimizing the delay and energy consumption. In this paper, the task offloading problem is formulated as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on multi-objective joint optimization using multi-agent deep reinforcement learning in a distributed architecture is proposed. The simulation results validate the efficacy of our model and algorithm, demonstrating that our proposed algorithm achieves better performance in minimizing comprehensive offloading costs.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389128624006339/pdfft?md5=db265398ca7ed57c38d747a29dd0706e&pid=1-s2.0-S1389128624006339-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312482","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":"Towards universal and transferable adversarial attacks against network traffic classification","authors":"","doi":"10.1016/j.comnet.2024.110790","DOIUrl":"10.1016/j.comnet.2024.110790","url":null,"abstract":"<div><div>In recent years, deep learning technology has shown astonishing potential in many fields, but at the same time, it also hides serious vulnerabilities. In the field of network traffic classification, attackers exploit this vulnerability to add designed perturbations to normal traffic, causing incorrect network traffic classification to implement adversarial attacks. The existing network traffic adversarial attack methods mainly target specific models or sample application scenarios, which have many problems such as poor transferability, high time cost, and low practicality. Therefore, this article proposes a method towards universal and transferable adversarial attacks against network traffic classification, which can not only perform universal adversarial attacks on all samples in the network traffic dataset, but also achieve cross data and cross model transferable adversarial attacks, that is, it has transferable attack effects at both the network traffic data and classification model levels. This method utilizes the geometric characteristics of the network model to design the target loss function and optimize the generation of universal perturbations, resulting in biased learning of features at each layer of the network model, leading to incorrect classification results. Meanwhile, this article conducted universality and transferability adversarial attack verification experiments on standard network traffic datasets of three different classification applications, USTC-TFC2016, ISCX2016, and CICIoT2023, as well as five common network models such as LeNet5. The results show that the proposed method performs universal adversarial attacks on five network models on three datasets, USTC-TFC2016, ISCX2016, and CICIoT2023, with an average attack success rate of over 80 %, 85 %, and 88 %, respectively, and an average time cost of about 0–0.3 ms; And the method proposed in this article has shown good transferable attack performance between five network models and on three network traffic datasets, with transferable attack rates approaching 100 % across different models and datasets, which is more closely related to practical applications.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319858","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":"Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm","authors":"","doi":"10.1016/j.comnet.2024.110802","DOIUrl":"10.1016/j.comnet.2024.110802","url":null,"abstract":"<div><p>The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as <span><math><mi>U</mi></math></span>, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (<span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></math></span>) and the near one (<span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span>), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (<span><math><mi>E</mi></math></span>) intending to intercept the <span><math><mrow><mi>U</mi><mo>−</mo><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241817","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":"6TiSCH IIoT network: A review","authors":"","doi":"10.1016/j.comnet.2024.110759","DOIUrl":"10.1016/j.comnet.2024.110759","url":null,"abstract":"<div><div>Low-power and Lossy Networks (LLN) constitute an interconnected network of numerous resource-constrained nodes, forming a wireless mesh network. The Time slotted Channel Hopping (TSCH) mode, introduced as a revision of the Medium Access Control (MAC) section within the IEEE 802.15.4 standard, stands as an emerging standard for industrial automation and process control. In 2013, the Internet Engineering Task Force (IETF) established the IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) working group (WG), defining the IPv6 deterministic wireless network—6TiSCH. This development is pivotal for advancing the broader adoption of IPv6 in industrial standards and facilitating the convergence of operational technology (OT) and information technology (IT). As of July 2023, the primary documents encompassing architecture, configuration and parameters, and Minimum Scheduling Function for the 6TiSCH protocol stack have been completed, and the status of the WG has transitioned from active to concluded. Over the past decade, the academic community has extensively researched protocol stacks related to 6TiSCH. This paper furnishes a comprehensive survey of the architecture and developmental processes underlying the 6TiSCH network, encapsulating research achievements since its inception, and delineating the challenges and prospective directions for its future development.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312483","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":"Efficient load distribution in heterogeneous vehicular networks using hierarchical controllers","authors":"","doi":"10.1016/j.comnet.2024.110805","DOIUrl":"10.1016/j.comnet.2024.110805","url":null,"abstract":"<div><p>Vehicle movement poses significant challenges in vehicular networks, often resulting in uneven traffic distribution. Fog computing (FC) addresses this by operating at the network edge, handling specific tasks locally instead of relying solely on cloud computing (CC) facilities. There are instances where FC may need additional resources and must delegate tasks to CC, leading to increased delay and response time. This work conducts a thorough examination of previous load balancing (LB) strategies, with a specific focus on software-defined networking (SDN) and machine learning (ML) based LB within the internet of vehicles (IoV). The insights derived from this research expedite the development of SDN controller-based LB solutions in the IoV network. The authors proposes the integration of a local SDN controller (LSDNC) within the FC tier to enable localized LB, addressing delay concerns. However, the information will be available to the main SDN controller (MSDNC) too. The authors explore the concept mathematically and simulates the formulated model and subjecting it to a comprehensive performance analysis. The simulation results demonstrate a significant reduction in delay, with a 125 ms difference when 200 onboard units (OBUs) are used, compared to conventional software-defined vehicular networks (SDVN). This improvement continues to increase as the number of OBUs grows. Our model achieves the same maximum throughput as the previous model but delivers faster response times, as decisions are made locally without the need to wait for the main controller.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241818","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}