2022 International Conference on Networking and Network Applications (NaNA)最新文献_第9页

An Encrypted Abnormal Stream Detection Method Based on Improved Skyline Computation 基于改进Skyline计算的加密异常流检测方法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00041

Xinghong Jiang, Xuan Li, Chenyang Lv, Yong Ma, Yulong Shen, Meibin He, Guozheng Li

{"title":"An Encrypted Abnormal Stream Detection Method Based on Improved Skyline Computation","authors":"Xinghong Jiang, Xuan Li, Chenyang Lv, Yong Ma, Yulong Shen, Meibin He, Guozheng Li","doi":"10.1109/NaNA56854.2022.00041","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00041","url":null,"abstract":"With the development of a new generation of mobile communication technology and the enhancement of user security awareness, a large amount of data containing private information generated by users every day will be transmitted in an encrypted form in the network, and it is difficult for traditional abnormal stream detection methods to detect encrypted data, which will increase the likelihood of DDoS attacks on servers that store user information. In response to this problem, this paper proposes a method called detection of encrypted abnormal stream based on improved skyline(DEF-IS). First of all, the Order-Revealing Encryption algorithm is used to encrypt the data stream to ensure the security of the data stream; Then, efficient encrypted abnormal data stream detection is carried out based on reservoir sampling algorithm and improved skyline algorithm; Finally, the performance of the DEF-IS algorithm is verified in the simulation environment. The experimental results show that DEF-IS algorithm can quickly and accurately detect abnormal data while ensuring the safety of data.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"128 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":"116049211","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

Committees: NaNA 2022 委员会:NaNA 2022

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/nana56854.2022.00006

引用次数: 0

Anteater: Malware Injection Detection with Program Network Traffic Behavior 食蚁兽:恶意软件注入检测与程序网络流量行为

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00036

Zuobin Ying, Yangzong Zhang, Shengmin Xu, Guowen Xu, Wenjian Liu

{"title":"Anteater: Malware Injection Detection with Program Network Traffic Behavior","authors":"Zuobin Ying, Yangzong Zhang, Shengmin Xu, Guowen Xu, Wenjian Liu","doi":"10.1109/NaNA56854.2022.00036","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00036","url":null,"abstract":"Recent stealth attacks conceal malicious behavior behind seemingly normal connections to popular online services provided by seemingly harmless applications. These attacks are undetectable using traditional network monitoring and signature-based detection techniques. Because attackers frequently use well-known cloud vendors to conceal C&C servers, anomalous traffic appears to be normal. In this paper, we propose an application-level monitoring system named “Anteater”. Our “Anteater” generates a fine-grained profile of each benign software's network traffic behavior, describing the “expected” network traffic behavior. By analyzing the program's network traffic configuration, our “Anteater” can quickly determine the IP address of the program's abnormal access and intercept it in real-time. “Anteater” was implemented in a real-world enterprise dataset containing over 400 million real-world network traffic sessions. The evaluation results indicate that “Anteater” has a high detection rate for malware injection, with a true positive rate of 94.5% and a false positive rate of less than 0.1%.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"91 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":"127559345","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

Research on Blockchain Usage for 5G Message Service 区块链在5G报文服务中的应用研究

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00092

Wang Ke, YaLing He, He Yuhuang

引用次数: 0

FedDBG: Privacy-Preserving Dynamic Benchmark Gradient in Federated Learning Against Poisoning Attacks FedDBG:针对中毒攻击的联邦学习中保护隐私的动态基准梯度

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00089

Mengfan Xu

{"title":"FedDBG: Privacy-Preserving Dynamic Benchmark Gradient in Federated Learning Against Poisoning Attacks","authors":"Mengfan Xu","doi":"10.1109/NaNA56854.2022.00089","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00089","url":null,"abstract":"The federated learning's (FL) ability to protect local data privacy while cooperatively training powerful global models has received extensive attention. Although some researchers have carried out researches on gradient privacy disclosure under poisoning attacks, the existing works still ignore the unreliability of initial data, which makes it difficult to obtain the benign initial reference gradient, resulting in a significant decline in the accuracy of the final global model. To solve this problem, we propose a privacy-preserving gradient framework in FL based on homomorphic encryption. The framework can ensure that malicious initial users and subsequent users cannot interfere with the accuracy of the global model by uploading the poisoning gradients. In this process, key parameters such as gradients of local users won't be leaked. We then design a dynamic reference gradient aggregation algorithm to mitigate the poisoning attack in FL, dynamically dividing the sub-gradients of each round of local uploads by clustering the gradients of different local uploads. Furthermore, the malicious and benign gradients are further separated and the optimal global model is obtained by iterative updating. We proved the security of the scheme theoretically, and verified the effectiveness of the scheme through experiments. The accuracy of the proposed scheme is at least 80% higher than that of the scheme without anti-poisoning measures.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"729 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":"116984349","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

A Traceability Method for Bitcoin Transactions Based on Gateway Network Traffic Analysis 基于网关网络流量分析的比特币交易溯源方法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00037

Dapeng Huang, Haoran Chen, Kai Wang, Chen Chen, Weili Han

{"title":"A Traceability Method for Bitcoin Transactions Based on Gateway Network Traffic Analysis","authors":"Dapeng Huang, Haoran Chen, Kai Wang, Chen Chen, Weili Han","doi":"10.1109/NaNA56854.2022.00037","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00037","url":null,"abstract":"Cryptocurrencies like Bitcoin have become a popular weapon for illegal activities. They have the characteristics of decentralization and anonymity, which can effectively avoid the supervision of government departments. How to de-anonymize Bitcoin transactions is a crucial issue for regulatory and judicial investigation departments to supervise and combat crimes involving Bitcoin effectively. This paper aims to de-anonymize Bitcoin transactions and present a Bitcoin transaction traceability method based on Bitcoin network traffic analysis. According to the characteristics of the physical network that the Bitcoin network relies on, the Bitcoin network traffic is obtained at the physical convergence point of the local Bitcoin network. By analyzing the collected network traffic data, we realize the traceability of the input address of Bitcoin transactions and test the scheme in the distributed Bitcoin network environment. The experimental results show that this traceability mechanism is suitable for nodes connected to the Bitcoin network (except for VPN, Tor, etc.), and can obtain 47.5% recall rate and 70.4% precision rate, which are promising in practice.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"68 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":"124173928","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

Research on DDoS Attack Detection Method Based on Deep Neural Network Model inSDN 基于深度神经网络模型inSDN的DDoS攻击检测方法研究

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00038

Wanqi Zhao, H. Sun, Dawei Zhang

引用次数: 1

A Dynamic Reconfiguration Method of Communication Groups for Parallel Volunteer Computing 面向并行志愿计算的通信组动态重构方法

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00093

Taiki Tomita, Keiichi Inohara, Yota Kurokawa, Masaru Fukushi

引用次数: 0

Message from the General Conference Chairs: NaNA 2022 大会主席的致辞:NaNA 2022

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/nana56854.2022.00005

Nana

引用次数: 0

Relation Transformation based on Graph Convolution Network for Entity Alignment 基于图卷积网络的实体对齐关系转换

2022 International Conference on Networking and Network Applications (NaNA) Pub Date : 2022-12-01 DOI: 10.1109/NaNA56854.2022.00060

Luheng Yang, Zhihui Wang, Tingting Zhu, Jianrui Chen

{"title":"Relation Transformation based on Graph Convolution Network for Entity Alignment","authors":"Luheng Yang, Zhihui Wang, Tingting Zhu, Jianrui Chen","doi":"10.1109/NaNA56854.2022.00060","DOIUrl":"https://doi.org/10.1109/NaNA56854.2022.00060","url":null,"abstract":"Entity alignment has lately received great attention for its key role in knowledge fusion, which has a goal that links entities with the same meaning from different knowledge graph. Most recently, many novel methods based on graph convolution network (GCN) have emerged as interesting models for entity alignment. Although these existing methods can further improve the quality of entity embedding, they ignored the influence of the embeddings of multiple relationships between entities on entity alignment. In addition, current models do not considerably learn the embeddings of relationship. To address these problems, we adopt a novel Relation Transformation based on Graph Convolution Network for entity alignment, named RT-GCN. Specifically, we point out a new GCN to aggregate the information of entities, which aims to strikingly obtain entities embeddings. Moreover, we develop a novel relational transformation method to generate relational embeddings. The crucial role of relational transformation is to strengthen the process of alignment and improve the robustness of the model. Experimental results on three datasets state that the proposed model RT-GCN performs surprisingly better than the state-of-the-art methods.","PeriodicalId":113743,"journal":{"name":"2022 International Conference on Networking and Network Applications (NaNA)","volume":"45 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":"116588051","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