International Conference on Data Science in Cyberspace最新文献

Message from the General Chairs of DSC 2020 2020年DSC主席致辞

International Conference on Data Science in Cyberspace Pub Date : 2020-07-01 DOI: 10.1109/dsc50466.2020.00005

Qing Li, M. Tamer Özsu, Hui Xiong

引用次数: 0

Keynote Talk #2 主题演讲#2

International Conference on Data Science in Cyberspace Pub Date : 2018-12-01 DOI: 10.1109/DESEC.2018.8625112

K. Nakao

引用次数: 0

Dsc#7 Dsc # 7

International Conference on Data Science in Cyberspace Pub Date : 2018-12-01 DOI: 10.1109/DESEC.2018.8625107

Koichiro Amemiya

引用次数: 0

Keynote Talk #1 主题演讲#1

International Conference on Data Science in Cyberspace Pub Date : 2018-12-01 DOI: 10.1109/DESEC.2018.8625120

J. Voas

引用次数: 0

Privacy-Preserving Range Query for High-Dimensional Uncertain Data in a Two-Party Scenario 双方场景下高维不确定数据的隐私保护范围查询

International Conference on Data Science in Cyberspace Pub Date : 1900-01-01 DOI: 10.1109/DSC49826.2021.9346235

Shenghao Su, Cheng Guo, Pengxu Tian, Xinyu Tang

{"title":"Privacy-Preserving Range Query for High-Dimensional Uncertain Data in a Two-Party Scenario","authors":"Shenghao Su, Cheng Guo, Pengxu Tian, Xinyu Tang","doi":"10.1109/DSC49826.2021.9346235","DOIUrl":"https://doi.org/10.1109/DSC49826.2021.9346235","url":null,"abstract":"With the fast evolution of sensor technology, massive high-dimensional data are collected by various service providers. Other institutions also want to use the data for analysis and statistics. However, for the privacy and legal concerns, data owners should not directly share the data with others. In addition, some factors such as measurement limitations, noise, and network delays may result in uncertain data. Compared with processing certain data, managing and processing uncertain data is more challenging. In this paper, we propose a privacy-preserving range query scheme for high-dimensional uncertain data owned by the other party, in which range query problem can be solved by data owners without revealing their data and the query range is invisible except the query requestor. We utilize the Paillier encryption as the basic block of our scheme. The data owner utilizes a binary tree index to promote query, which combines pivot-mapping and Bloom filter. We analyze the security and evaluate the performance of the scheme with a synthetic dataset. The analysis and experimental results show that our scheme is secure and efficient.","PeriodicalId":195208,"journal":{"name":"International Conference on Data Science in Cyberspace","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115204429","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 Simple Method for Locating Topic Sources in Uncertainty Diffusion Networks 不确定性扩散网络中主题源定位的一种简单方法

International Conference on Data Science in Cyberspace Pub Date : 1900-01-01 DOI: 10.1109/DSC.2016.44

Jianyi Huang, Chungjin Hu, Mingzhe Fang, Tong Wu, Peng Shi

{"title":"A Simple Method for Locating Topic Sources in Uncertainty Diffusion Networks","authors":"Jianyi Huang, Chungjin Hu, Mingzhe Fang, Tong Wu, Peng Shi","doi":"10.1109/DSC.2016.44","DOIUrl":"https://doi.org/10.1109/DSC.2016.44","url":null,"abstract":"The online social network has become an important information transmitting media. Finding the sources of topic on the online social networks (OSNs), which helps us to know the cause of events, to identify the authenticity of the topic and to target the origin of rumors, is very important. However, topic sources locating is difficult because the structure of OSNs is complex and the complete OSNs are hard to observe. At the same time, the fact that the topic diffusion process is multiple-source, asynchronous and uncertain, causes topic sources locating to be more difficult. In this paper we aim to solve the problem of the topic sources locating in the uncertainty diffusion networks. We first analyzed the uncertainty diffusion relationship in detail through the real data sets. Then according to the characteristics of incomplete observation and asynchronous transmission, we propose a dynamic locating method based on the activation time and the partial topology structure from a subnet transition to earlier subnet. Our method needn't build the underlying network topology in advance. Experimental results on BA networks show that our method can solve the topic sources locating problem of asynchronous information diffusion with incomplete observation.","PeriodicalId":195208,"journal":{"name":"International Conference on Data Science in Cyberspace","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129385703","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

A DNS-based Data Exfiltration Traffic Detection Method for Unknown Samples 基于dns的未知样本数据泄露流量检测方法

International Conference on Data Science in Cyberspace Pub Date : 1900-01-01 DOI: 10.1109/DSC55868.2022.00032

Ruiling Gan, Jiawen Diao, Xiang Cui, Shouyou Song

{"title":"A DNS-based Data Exfiltration Traffic Detection Method for Unknown Samples","authors":"Ruiling Gan, Jiawen Diao, Xiang Cui, Shouyou Song","doi":"10.1109/DSC55868.2022.00032","DOIUrl":"https://doi.org/10.1109/DSC55868.2022.00032","url":null,"abstract":"The advanced persistent threat (APT) is one of the most serious threats to cyberspace security. Posting back of exfiltrated data by way of DNS covert channels has become increasingly popular among APT attackers. Early detection techniques were mainly based on rule matching, whose accuracy may be affected by the subjectivity of the researchers. The rise of machine learning technology solves this problem. However, the current DNS traffic detection models based on machine learning lack the open-source datasets for training and they will lose detection accuracy for unknown malicious traffic whose abnormal points are different from the observed samples. As for the problem of insufficient data set, we propose a sample set enhancement method that simulating DNS attacks in the cyber range and using the captured flow as the training set. Regarding the detection of unknown malicious traffic, we put forward four new features (domain readability, domain structure, second-level domain phishing and IP discreteness) based on the principles for constructing malicious traffic. We use the decision tree algorithm to implement a detection model. In the unknown DNS data leakage traffic tests, our model achieved an average detection rate of 99.925%. After applying our sample set and feature set enhanced schemes to the existing work, the experimental results show that the enhanced detection model can detect unknown DNS data leakage traffic that could not be detected previously.","PeriodicalId":195208,"journal":{"name":"International Conference on Data Science in Cyberspace","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115847776","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: 2

Capability-based Component Security Mechanism for Microkernel OS 基于功能的微内核操作系统组件安全机制

International Conference on Data Science in Cyberspace Pub Date : 1900-01-01 DOI: 10.1109/DSC55868.2022.00012

Jiajia Yi, Mengmeng Li, Kun Xiao, Lirong Chen, Lei Luo, R. Xu

引用次数: 0

Detection and Defense of SYN Flood Attacks Based on Dual Stack Network Firewall 基于双栈网络防火墙的SYN Flood攻击检测与防御

International Conference on Data Science in Cyberspace Pub Date : 1900-01-01 DOI: 10.1109/DSC.2016.108

Pengfule Ding, Zhihong Tian, Hongli Zhang, Yong Wang, L. Zhang, Sanchuan Guo

引用次数: 5

Visualization Study of High-Dimensional Data Classification Based on PCA-SVM 基于PCA-SVM的高维数据分类可视化研究

International Conference on Data Science in Cyberspace Pub Date : 1900-01-01 DOI: 10.1109/DSC.2017.57

Zhongwen Zhao, Huanghuang Guo

引用次数: 7