Computer Networks最新文献_第6页

An Internet of Vehicles road traffic data sharing scheme based on signcryption and editable blockchain 基于签名加密和可编辑区块链的车联网道路交通数据共享方案

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-19 DOI: 10.1016/j.comnet.2024.110864

Xueyan Liu, Xin Xiong, Jia Wang, Yujiao Qi

引用次数: 0

SecureEI: Proactive intellectual property protection of AI models for edge intelligence SecureEI：边缘智能人工智能模型的主动知识产权保护

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-18 DOI: 10.1016/j.comnet.2024.110825

Peihao Li , Jie Huang , Shuaishuai Zhang , Chunyang Qi

{"title":"SecureEI: Proactive intellectual property protection of AI models for edge intelligence","authors":"Peihao Li , Jie Huang , Shuaishuai Zhang , Chunyang Qi","doi":"10.1016/j.comnet.2024.110825","DOIUrl":"10.1016/j.comnet.2024.110825","url":null,"abstract":"<div><div>Deploying AI models on edge computing platforms enhances real-time performance, reduces network dependency, and ensures data privacy on terminal devices. However, these advantages come with increased risks of model leakage and misuse due to the vulnerability of edge environments to physical and cyber attacks compared to cloud-based solutions. To mitigate these risks, we propose SecureEI, a proactive intellectual property protection method for AI models that leverages model splitting and data poisoning techniques. SecureEI divides the model into two components: DeviceNet, which processes input data into protected license data, and EdgeNet, which operates on the license data to perform the intended tasks. This method ensures that only the transformed license data yields high model accuracy, while original data remains unrecognizable, even under fine-tuning attacks. We further employ targeted training strategies and weight adjustments to enhance the model’s resistance to potential attacks that aim to restore its recognition capabilities for original data. Evaluations on MNIST, Cifar10, and FaceScrub datasets demonstrate that SecureEI not only maintains high model accuracy on license data but also significantly bolsters defense against fine-tuning attacks, outperforming existing state-of-the-art techniques in safeguarding AI intellectual property on edge platforms.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110825"},"PeriodicalIF":4.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533934","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}

引用次数: 0

Enhancing smart city IoT communication: A two-layer NOMA-based network with caching mechanisms and optimized resource allocation 增强智慧城市物联网通信：具有缓存机制和优化资源分配的基于 NOMA 的双层网络

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-18 DOI: 10.1016/j.comnet.2024.110857

Tareq M. Alkhaldi , Abdulbasit A. Darem , Asma A. Alhashmi , Tawfik Al-Hadhrami , Azza Elneil Osman

{"title":"Enhancing smart city IoT communication: A two-layer NOMA-based network with caching mechanisms and optimized resource allocation","authors":"Tareq M. Alkhaldi , Abdulbasit A. Darem , Asma A. Alhashmi , Tawfik Al-Hadhrami , Azza Elneil Osman","doi":"10.1016/j.comnet.2024.110857","DOIUrl":"10.1016/j.comnet.2024.110857","url":null,"abstract":"<div><div>With advancements in next-generation communication systems, large-scale Internet of Things device (IoTDs) deployments in smart cities face challenges like limited bandwidth, high latency, and network congestion. To address this, we propose a two-layer network architecture utilizing non-orthogonal multiple access (NOMA) and caching to enhance IoT communications’ performance, efficiency, and reliability. Our primary objective is to optimize resource allocation and solve the association problem in a two-layer network. We formulated a joint optimization problem to maximize system utility through device association, power, and bandwidth allocation, considering constraints like channel quality and interference. We decoupled the non-linear, non-convex problem using block coordinate descent (BCD) and inner approximation techniques to maximize the aggregated data rate in high-density IoT scenarios. This approach reduced computational complexity while proving the scheme’s theoretical and numerical convergence. To evaluate the proposed scheme, we compared its performance with an ideal backhauling approach, an exhaustive search (upper bound), and a Genetic algorithm-based heuristic. Our scheme outperformed the others, achieving 98.04% of the ideal backhauling and 99.60% of the upper bound. Statistical analysis confirmed its robustness and consistent performance across various conditions. The two-layer NOMA-based network with caching and optimized resource allocation significantly enhances IoT communication efficiency and resilience, offering a solid framework for future smart city deployments.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110857"},"PeriodicalIF":4.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534633","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}

引用次数: 0

Component-based modeling of cascading failure propagation in directed dual-weight software networks 基于组件的有向双重软件网络级联故障传播建模

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-17 DOI: 10.1016/j.comnet.2024.110861

Qiyuan Li, Yumeng Wang, Donghai Tian, Chong Yuan, Changzhen Hu

{"title":"Component-based modeling of cascading failure propagation in directed dual-weight software networks","authors":"Qiyuan Li, Yumeng Wang, Donghai Tian, Chong Yuan, Changzhen Hu","doi":"10.1016/j.comnet.2024.110861","DOIUrl":"10.1016/j.comnet.2024.110861","url":null,"abstract":"<div><div>Software vulnerabilities often lead to cascading failures, resulting in service unavailability and potential breaches of user data. However, existing models for cascading failure propagation typically focus solely on the static design’s calling relationships, disregarding dynamic runtime propagation paths. Moreover, current network topology models primarily consider function calling frequency while overlooking critical factors like internal failure probability and component failure tolerance rates. Yet, these factors significantly influence the actual propagation of software cascading failures. In this study, we address these limitations by incorporating internal failure probabilities and calling frequencies as node and edge weights, respectively. This forms the basis of our component-based directed dual-weight software network cascading failure propagation model. This model encompasses the evaluation of cascading failure propagation through intra-component and inter-component propagation probabilities, alongside the constraint of component failure tolerance rates. Through extensive experiments conducted on six real-world software applications, our model has demonstrated its effectiveness in predicting software cascading failure propagation processes. This method deepens our understanding of software failures and structures, equipping software testers with the knowledge to make well-informed judgments regarding software quality concerns.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110861"},"PeriodicalIF":4.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533946","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}

引用次数: 0

LoCoNOMA: A grant-free resource allocation for massive MTC LoCoNOMA：大规模 MTC 的无赠款资源分配

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-16 DOI: 10.1016/j.comnet.2024.110859

Ibtissem Oueslati , Oussama Habachi , Jean-Pierre Cances , Vahid Meghdadi

引用次数: 0

CLLS: Efficient certificateless lattice-based signature in VANETs CLLS：VANET 中基于网格的高效无证书签名

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-16 DOI: 10.1016/j.comnet.2024.110858

Sheng-wei Xu , Shu-han Yu , Zi-Yan Yue , Yi-Long Liu

{"title":"CLLS: Efficient certificateless lattice-based signature in VANETs","authors":"Sheng-wei Xu , Shu-han Yu , Zi-Yan Yue , Yi-Long Liu","doi":"10.1016/j.comnet.2024.110858","DOIUrl":"10.1016/j.comnet.2024.110858","url":null,"abstract":"<div><div>The rapid development of Vehicular Ad-hoc Network (VANETs) has improved road safety and traffic management, and brought great convenience to intelligent transportation system (ITS). However, the transmission of data over open channels caused many security issues. Certificateless cryptography solves the certificate management and key escrow problems, which makes it the primary method for message authentication in VANETs. However, with the emergence of quantum computing, traditional cryptography faces a significant challenge. Lattice-based cryptography are regarded as effective post-quantum ciphers. Nevertheless, nearly all existing lattice-based certificateless signature schemes rely on Gaussian sampling or trapdoor techniques, resulting in computational inefficiencies and large key and signature sizes that are impractical for VANETs. To address these issues, we proposed the first efficient algebraic lattice-based certificateless signature scheme in VANETs based on the Dilithium signature algorithm. The security of our certificateless lattice-based signature(CLLS) scheme is based on the MSIS and MLWE hardness assumption, which makes the scheme resistant to quantum attacks and easy to implement. Our scheme did not use Gaussian sampling or trapdoor techniques, which improve the computational and storage efficiency. As a result, the public key of our scheme is 1X smaller than the previous scheme and the size of signature is 2X smaller than the previous efficient algebraic lattice scheme. In addition, compared to the most efficient existing CLLS scheme, the signing and verification computation cost of our scheme are reduced by 20% and 55% respectively and our proposed CLLS scheme has low power consumption. Furthermore, the security of our scheme achieves strong unforgeability against chosen-message attacks(SUF-CMA) in the random oracle model(ROM), which surpasses that of existing lattice-based certificateless signature schemes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110858"},"PeriodicalIF":4.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534631","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}

引用次数: 0

Mitigating critical nodes in brain simulations via edge removal 通过去除边缘缓解大脑模拟中的关键节点

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-16 DOI: 10.1016/j.comnet.2024.110860

Yubing Bao , Xin Du , Zhihui Lu , Jirui Yang , Shih-Chia Huang , Jianfeng Feng , Qibao Zheng

{"title":"Mitigating critical nodes in brain simulations via edge removal","authors":"Yubing Bao , Xin Du , Zhihui Lu , Jirui Yang , Shih-Chia Huang , Jianfeng Feng , Qibao Zheng","doi":"10.1016/j.comnet.2024.110860","DOIUrl":"10.1016/j.comnet.2024.110860","url":null,"abstract":"<div><div>Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110860"},"PeriodicalIF":4.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533935","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}

引用次数: 0

Network anomaly detection in cars: A case for time-sensitive stream filtering and policing 汽车网络异常检测：时间敏感流过滤和监控案例

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-15 DOI: 10.1016/j.comnet.2024.110855

Philipp Meyer, Timo Häckel, Sandra Reider, Franz Korf, Thomas C. Schmidt

{"title":"Network anomaly detection in cars: A case for time-sensitive stream filtering and policing","authors":"Philipp Meyer, Timo Häckel, Sandra Reider, Franz Korf, Thomas C. Schmidt","doi":"10.1016/j.comnet.2024.110855","DOIUrl":"10.1016/j.comnet.2024.110855","url":null,"abstract":"<div><div>Connected vehicles are threatened by cyber-attacks as in-vehicle networks technologically approach (mobile) LANs with several wireless interconnects to the outside world. Malware that infiltrates a car today faces potential victims of constrained, barely shielded Electronic Control Units (ECUs). Many ECUs perform critical driving functions, which stresses the need for hardening security and resilience of in-vehicle networks in a multifaceted way. Future vehicles will comprise Ethernet backbones that differentiate services via Time-Sensitive Networking (TSN). The well-known vehicular control flows will follow predefined schedules and TSN traffic classifications. In this paper, we exploit this traffic classification to build a network anomaly detection system. We show how filters and policies of TSN can identify misbehaving traffic and thereby serve as distributed guards on the data link layer. On this lowest possible layer, our approach derives a highly efficient network protection directly from TSN. We classify link layer anomalies and micro-benchmark the detection accuracy in each class. Based on a topology derived from a real-world car and its traffic definitions we evaluate the detection system in realistic macro-benchmarks based on recorded attack traces. Our results show that the detection accuracy depends on how exact the specifications of in-vehicle communication are configured. Most notably for a fully specified communication matrix, our anomaly detection remains free of false-positive alarms, which is a significant benefit for implementing automated countermeasures in future vehicles.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110855"},"PeriodicalIF":4.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534632","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}

引用次数: 0

Adaptive learning-based hybrid recommender system for deception in Internet of Thing 基于自适应学习的混合推荐系统，用于物联网中的欺骗行为

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-11 DOI: 10.1016/j.comnet.2024.110853

Volviane Saphir Mfogo , Alain Zemkoho , Laurent Njilla , Marcellin Nkenlifack , Charles Kamhoua

{"title":"Adaptive learning-based hybrid recommender system for deception in Internet of Thing","authors":"Volviane Saphir Mfogo , Alain Zemkoho , Laurent Njilla , Marcellin Nkenlifack , Charles Kamhoua","doi":"10.1016/j.comnet.2024.110853","DOIUrl":"10.1016/j.comnet.2024.110853","url":null,"abstract":"<div><div>In the rapidly evolving Internet of Things (IoT) security domain, device vulnerabilities pose significant risks, frequently exploited by cyberattackers. Traditional reactive security measures like patching often fall short against advanced threats. This paper introduces a proactive deception system enhanced by an innovative Adaptive Learning-based Hybrid Recommender System (AL-HRS), utilizing the vulnerability and attack repository for IoT (VARIoT) database. This advanced system identifies existing vulnerabilities and dynamically recommends additional deceptive vulnerabilities based on real-time analysis of attacker behavior and historical exploit data. These recommended vulnerabilities mislead attackers into engaging with controlled environments such as honeypots, effectively neutralizing potential threats. The AL-HRS combines the predictive strengths of content-based filtering (CBF) and collaborative filtering (CF) with an adaptive learning mechanism that adjusts recommendations based on ongoing attacker interactions, ensuring the system’s efficacy amidst changing attack patterns. Our approach innovatively combines these methodologies to provide a continuously evolving security strategy, significantly enhancing the deception capability of IoT systems. Initial evaluations demonstrate a potential reduction in device compromise, highlighting the effectiveness and strategic relevance of this adaptive deception framework in IoT cybersecurity.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110853"},"PeriodicalIF":4.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534629","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}

引用次数: 0

AI/ML-based services and applications for 6G-connected and autonomous vehicles 为 6G 互联和自动驾驶汽车提供基于 AI/ML 的服务和应用

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-11 DOI: 10.1016/j.comnet.2024.110854

Claudio Casetti , Carla Fabiana Chiasserini , Falko Dressler , Agon Memedi , Diego Gasco , Elad Michael Schiller

{"title":"AI/ML-based services and applications for 6G-connected and autonomous vehicles","authors":"Claudio Casetti , Carla Fabiana Chiasserini , Falko Dressler , Agon Memedi , Diego Gasco , Elad Michael Schiller","doi":"10.1016/j.comnet.2024.110854","DOIUrl":"10.1016/j.comnet.2024.110854","url":null,"abstract":"<div><div>AI and ML emerge as pivotal in overcoming the limitations of traditional network optimization techniques and conventional control loop designs, particularly in addressing the challenges of high mobility and dynamic vehicular communications inherent in the domain of connected and autonomous vehicles (CAVs). The survey explores the contributions of novel AI/ML techniques in the field of CAVs, also in the context of innovative deployment of multilevel cloud systems and edge computing as strategic solutions to meet the requirements of high traffic density and mobility in CAV networks. These technologies are instrumental in curbing latency and alleviating network congestion by facilitating proximal computing resources to CAVs, thereby enhancing operational efficiency also when AI-based applications require computationally-heavy tasks. A significant focus of this survey is the anticipated impact of 6G technology, which promises to revolutionize the mobility industry. 6G is envisaged to foster intelligent, cooperative, and sustainable mobility environments, heralding a new era in vehicular communication and network management. This survey comprehensively reviews the latest advancements and potential applications of AI/ML for CAVs, including sensory perception enhancement, real-time traffic management, and personalized navigation.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110854"},"PeriodicalIF":4.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534630","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}

引用次数: 0