2022 International Conference on Networking and Network Applications (NaNA)最新文献_第2页

A Secure Authentication Mechanism for Multi-Dimensional Identifier Network 多维标识网络的安全认证机制

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00035

Yuan Cheng, Shuai Gao, Xindi Hou

引用次数: 0

Research on grassland grazing environment planning based on GA-ACO hybrid algorithm 基于GA-ACO混合算法的草地放牧环境规划研究

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00051

Shi Xiangnan, Yanbo Yang, Qiwei Xu, Teng Li, Jiawei Zhang

{"title":"Research on grassland grazing environment planning based on GA-ACO hybrid algorithm","authors":"Shi Xiangnan, Yanbo Yang, Qiwei Xu, Teng Li, Jiawei Zhang","doi":"10.1109/NaNA56854.2022.00051","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00051","url":null,"abstract":"Aiming at the problems of overgrazing by pastoralists and the reduction of grassland utilization rate, a Genetic Algorithm-Ant Colony Optimization (GA-ACO) hybrid algorithm was proposed to solve the problem of grazing in pastoral areas. Firstly, the pastoral environment is analyzed, and the overgrazing area and the usable area of the pastoral area are obtained by calculation, and the three types of operators in the genetic algorithm are designed according to the stock carrying capacity, the number of sheep and the grazing days as the standard for dividing the grazing area. Connectivity concept, so as to ensure that the algorithm reasonably divides the pastoral rotation area. Then, on this basis, the information backtracking mechanism and dynamic detection mechanism of the ant colony algorithm are improved, so as to improve the convergence speed of the algorithm and quickly find the shortest path for grazing in grassland pastoral areas. Finally, in order to verify the effectiveness of the hybrid algorithm, random generation maps of different sizes of pastoral areas are used for algorithm simulation experiments. The experimental results show that the algorithm can reasonably divide the rotation grazing area, effectively avoid the overgrazing area, and quickly plan the shortest grazing path, which is the scientific basis for the unmanned aerial vehicle.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124501413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differential Preserving in XGBoost Model for Encrypted Traffic Classification 加密流量分类中XGBoost模型的差分保持

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00044

Zhe Wang, Baihe Ma, Yong Zeng, Xiaojie Lin, Kaichao Shi, Ziwen Wang

引用次数: 1

MARL-MOTAG: Multi-Agent Reinforcement Learning Based Moving Target Defense to thwart DDoS attacks MARL-MOTAG:基于多智能体强化学习的移动目标防御，阻止DDoS攻击

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00061

Zhuoyuan Li, Zan Zhou, Tao Zhang, Xiaolin Xing

{"title":"MARL-MOTAG: Multi-Agent Reinforcement Learning Based Moving Target Defense to thwart DDoS attacks","authors":"Zhuoyuan Li, Zan Zhou, Tao Zhang, Xiaolin Xing","doi":"10.1109/NaNA56854.2022.00061","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00061","url":null,"abstract":"The popularity of intelligent methods has expanded the means of DDoS attacks, which has significantly impacted online services. The static defense mechanism lacks the resistance to flooding, and the moving target defense has become an effective method to defend against distributed denial of service (DDoS) attacks. In order to adapt dynamic defense according to network conditions, while reducing resource consumption. In this paper, we propose a multi-agent reinforcement learning system (MARL-MOTAG) based on the MOTAG system, which can adaptively make decisions based on the server status. MA-MOTAG retains the proxy server settings of MOTAG and separates the proxy server into two clusters according to the degree of damage. The resource consumption caused by user migration is reduced through the new shuffling mechanism. At the same time, multi-agent reinforcement learning reduces the complexity of the action space and can quickly feedback and adaptively divide server clusters for complex network environments. Simulation results show that the proposed algorithm can converge better and resist DDoS attacks while reducing migration resource consumption.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129469784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Rate Fairness Maximization of Vehicular Networks: A Deep Reinforcement Learning Approach 基于深度强化学习的车辆网络公平性最大化研究

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00027

Shenghui Zhao, Bao Gui, Guilin Chen, Bin Yang

引用次数: 0

An Energy-Efficient and Obstruction-Free Design Scheme for FSO-based Data Center Network 基于fso的数据中心网络节能无阻碍设计方案

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00031

Wenning Lu, Bingbing Li

{"title":"An Energy-Efficient and Obstruction-Free Design Scheme for FSO-based Data Center Network","authors":"Wenning Lu, Bingbing Li","doi":"10.1109/NaNA56854.2022.00031","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00031","url":null,"abstract":"With the rapid growth of Internet applications, the massive demand for cloud services has brought challenges to data center networks (DCNs). Traditional wired DCNs adopt multi-layer structure, which makes high-layers switches easily congested. Furthermore, complex cabling in wired DCNs affects the flow of cold air, reduces the cooling efficiency and increases energy consumption. To reduce flow congestion and power consumption problems and eliminate complex wiring, in this paper we introduce an architecture for intra-DCN based on free space optic (FSO) wireless communication. The proposed scheme exploits spatial position of FSO transceivers at different heights to realize obstruction-free transmission for inter-rack requests. We formulate the transceiver arrangement problem into a mathematical model while satisfying the requirement for direct line-of-sight communication between any pair of transceiver nodes. Moreover, we evaluate the performance of the proposed scheme via simulation in terms of flow completion time, server throughput, and power consumption. Compared with traditional ones, our scheme can considerably decrease power consumption while obtaining better network performance.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128764910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Layered classification method for darknet traffic based on Weighted K-NN 基于加权K-NN的暗网流量分层分类方法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00045

Kaichao Shi, Baihe Ma, Yong Zeng, Xiaojie Lin, Zhe Wang, Ziwen Wang

{"title":"Layered classification method for darknet traffic based on Weighted K-NN","authors":"Kaichao Shi, Baihe Ma, Yong Zeng, Xiaojie Lin, Zhe Wang, Ziwen Wang","doi":"10.1109/NaNA56854.2022.00045","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00045","url":null,"abstract":"In recent years, anonymous networks are used very frequently. Difficulties in tracking user's identities increase with the frequent usage of anonymity networks. This increases the difficulty of detecting cybercriminal activity. To prevent crime, darknet traffic needs to be monitored. Most of the existing dark-net researches focuses on the Tor without adequate consideration of other darknets. Moreover, most of the work content focuses on distinguishing normal traffic and darknet traffic, and lacks a fine-grained classification method for darknet traffic. This paper proposes a hierarchical classification method for the network traffic of FreeNet, one of the most frequently used darknets, which can distinguish between normal traffic and FreeNet traffic, as well as five FreeNet user behaviors. We train the classifier based on the weighted K-NN. The experimental results show that the proposed classifier distinguishes normal traffic from FreeNet traffic with an average accuracy of 99.6% and five user behaviors with an average accuracy of 95.8%. We compared our classifier with existing works such as decision tree (DT), Gaussian naive Bayes (Gaussian NB), and K-NN. The results show that the accuracy of the classifier is the highest when distinguishing user behavior. Compared with the above three models, the accuracy of the classifier is improved by 1.86%, 57.95%, and 3.10% respectively.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128780665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Design of an OTN-based Failure/Alarm Propagation Simulator 基于otn的故障/报警传播模拟器的设计

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00099

Zening Li, P. Ho, Yan Jiao, Bingbing Li, Yuren You

引用次数: 1

Link-ability: A Prediction Method of Web Service Compatibility based on Markov Process 链接能力:基于马尔可夫过程的Web服务兼容性预测方法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00094

Shiyang Ma, Yahui Li, Zhonghua Wang, Di Lu, Xuewen Dong, Wei Tong, Lingxiao Yang

{"title":"Link-ability: A Prediction Method of Web Service Compatibility based on Markov Process","authors":"Shiyang Ma, Yahui Li, Zhonghua Wang, Di Lu, Xuewen Dong, Wei Tong, Lingxiao Yang","doi":"10.1109/NaNA56854.2022.00094","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00094","url":null,"abstract":"Prediction on compatibility of a service is crucial because it not only provides assurance for service requesters to successfully fulfil the workflow, but also relieves the pressure of the service pool from overload. Existing researches on parameter prediction mainly rely on collaborative filtering and matrix factorization, which are easily exposed to data-sparse problem, and they don't have solid support from mathematical proof. In this paper, we are the first to propose a method to predict compatibility with reliable mathematical proof, and its mechanism prevents data-sparse. Firstly we propose an architecture called Topology Retrievable Service Oriented Architecture, which is capable of collecting individual invocation records in a specific period of time and generate the whole topology of the service pool. Secondly, based on the architecture we presented, we give our definitions of Link-ability, propose Link-ability Generation Algorithm and present our solid mathematical strategies in the aspect of Markov Process. Lastly, we perform a series of experiment to strengthen our point, the result shows that our Link-ability not only reveals compatibility, but also potential compatibility of connecting services.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114197268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EEG-Based Fatigue Detection Using PLI Brain Network and Relief Algorithm 基于PLI脑网络和救济算法的脑电图疲劳检测

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00058

Yan He, Zhongmin Wang, Yupeng Zhao

{"title":"EEG-Based Fatigue Detection Using PLI Brain Network and Relief Algorithm","authors":"Yan He, Zhongmin Wang, Yupeng Zhao","doi":"10.1109/NaNA56854.2022.00058","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00058","url":null,"abstract":"EEG-based fatigue driving monitoring has important application value in road traffic safety, and the ultimate goal of the research is the development and use of wearable devices, and too many EEG channels in practical application scenarios is detrimental to device portability, and it will lead to problems such as large amount of data, complex calculation and long processing time, so it is especially important to study how to select the EEG channels highly correlated with fatigue. In this paper, a PLI-Relief-based channel selection algorithm by combining the PLI functional connectivity and the weighting idea of Relief algorithm is proposed, and it is applied to the channel selection of fatigue driving EEG. First, the PLI functional connectivity matrix is constructed for the EEG signals after preprocessing, and the binarized PLI matrix is mapped into a brain functional network, and the prime channels are selected by the degree property of the brain network. Then, the power spectral density features are extracted from the EEG signals of the prime channels, and the weights of each prime channel are obtained using the relief algorithm, then the number and the names of optimal channels are determined according to the recognition accuracy of different channel combinations. The proposed method was validated on the publicly available SEED-VIG dataset, and the data of the seven optimal channels is finally selected and obtains a classification accuracy of 81.25%. The framework proposed in this paper takes into account both the correlation between channels and the characteristics of the channel signals themselves in channel selection, which is a reference value for the development and application of wearable devices.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"62 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131639428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0