2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)最新文献

An APT Attack Analysis Framework Based on Self-define Rules and Mapreduce 基于自定义规则和Mapreduce的APT攻击分析框架

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00017

Yulu Qi, Rong Jiang, Yan Jia, Aiping Li

{"title":"An APT Attack Analysis Framework Based on Self-define Rules and Mapreduce","authors":"Yulu Qi, Rong Jiang, Yan Jia, Aiping Li","doi":"10.1109/DSC50466.2020.00017","DOIUrl":"https://doi.org/10.1109/DSC50466.2020.00017","url":null,"abstract":"The essence of Internet security is information security, as more and more industries rely on the Internet, in order to protect the information security of these industries, spawned local area networks (LANs), intranets and so on. With the development of information sensor technology, the Internet of Things (IoT) that interconnects physical devices has emerged. As a unity of computing process and physical process, the Cyberphysical systems (CPS) is the next generation intelligent system which integrates computing, communication and control. CyberPhysical systems cover a wide range of applications, including intelligent transportation systems, telemedicine, smart grids, aerospace, and many other fields, many of which involve critical infrastructure. The APT attacks are typically directed against these critical infrastructures around the world. So, timely and accurate detection APT attacks and take effective defensive measures, it is meaningful to protect the national information security. Although APT attacks seem destructive, their attack process are complex and changeable, in essence, they usually follow certain rules. This paper proposes an APT attack analysis framework based on the APT attack rules and current mainstream detection technologies. The framework iteratively matches the collected data with the cyber security knowledge graph, and implements constraints relies on the cyber security knowledge graph and self-defined attack rules, thereby realizing the current security status of the network in real time.","PeriodicalId":423182,"journal":{"name":"2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115005909","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: 4

Which DGA Family does A Malicious Domain Name Belong To 恶意域名属于哪个DGA族

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00016

Yunyi Zhang, Yuelong Wu, Shuyuan Jin

引用次数: 1

Refining Co-operative Competition of Robocup Soccer with Reinforcement Learning 用强化学习改进机器人足球合作竞赛

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00049

Zhengqiao Wang, Yufan Zeng, Yue Yuan, Yibo Guo

引用次数: 1

The Class Overlap Model for System Log Anomaly Detection Based on Ensemble Learning 基于集成学习的系统日志异常检测类重叠模型

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00064

Yitong Ren, Zhaojun Gu, Lanlan Pan, Chunbo Liu

引用次数: 1

Incorporating Entity Type Information into Knowledge Representation Learning 将实体类型信息纳入知识表示学习

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00023

Wenyu Huang, Guohua Wang, Huakui Zhang, Feng Chen

引用次数: 0

A Parallel Algorithm for Graph Transaction Based Frequent Subgraph Mining 基于频繁子图挖掘的图事务并行算法

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00061

Fengcai Qiao, Yuanfa Zhang, Jinsheng Deng, Zhaoyun Ding, Aiping Li

{"title":"A Parallel Algorithm for Graph Transaction Based Frequent Subgraph Mining","authors":"Fengcai Qiao, Yuanfa Zhang, Jinsheng Deng, Zhaoyun Ding, Aiping Li","doi":"10.1109/DSC50466.2020.00061","DOIUrl":"https://doi.org/10.1109/DSC50466.2020.00061","url":null,"abstract":"Frequent subgraph patterns play an important role in feature mining for graph data. The problem of mining these patterns is defined as finding subgraphs that appear frequently according to a given frequency threshold. Usually, frequent subgraph mining (FSM) is conducted in graph transaction setting, in which graph database contains many small graphs. Since multicore processors are quite popular this day, many algorithms can be accelerated with multi-thread technique. This paper proposed a multi-thread frequent subgraph mining algorithm and achieved considerable acceleration in the experiments. In this paper, a parallel frequent subgraph mining algorithm named PTRGRAM (Parallel Transaction based Graph Mining) which can take full advantage of the multi-core performance of current processors was proposed. In the algorithm, the data synchronization between multiple threads is based on the producer-consumer model. In addition, to speed the support computing, the embedding node list is introduced for optimization. Finally, experimental performance evaluations were conducted with two graph datasets, demonstrating that the proposed algorithm outperforms the single-threaded gSpan and FFSM algorithm.","PeriodicalId":423182,"journal":{"name":"2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114161215","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: 3

Tweet Stance Detection: A Two-stage DC-BILSTM Model Based on Semantic Attention 推文姿态检测:基于语义注意的两阶段DC-BILSTM模型

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00012

Yuanyu Yang, Bin Wu, Kai Zhao, Wenying Guo

{"title":"Tweet Stance Detection: A Two-stage DC-BILSTM Model Based on Semantic Attention","authors":"Yuanyu Yang, Bin Wu, Kai Zhao, Wenying Guo","doi":"10.1109/DSC50466.2020.00012","DOIUrl":"https://doi.org/10.1109/DSC50466.2020.00012","url":null,"abstract":"Stance classification in tweet aims at detecting whether the author of the tweet is in FAVOR of, AGAINST, or NONE towards a pre-chosen target entity. Recently proposed Densely Connected BI-LSTM can effectively relieve overfitting and vanishing-gradient problems as well as dealing with long-term dependencies during multi-layer LSTM training. Based on this, we propose a two-stage deep attention neural network(T-DAN) for target-specific stance detection. This model employs densely connected BI-LSTM to encode tweet tokens and traditional bidirectional LSTM to encode target tokens. Besides, we decompose this ternary classification problem into two binary classification problems to mitigating the imbalanced distribution of labels. In the first stage, we find out the tweet is neutral or subjective about the specific target. In the second stage, we classify the stance of a given subjective tweet’s stance. Moreover, we propose a novel method of attention calculation based on the semantic similarity of tweet tokens and target tokens which can locate the crucial words related to target. Experimental results on English and Chinese datasets demonstrate that our proposed method surpasses some strong baselines and achieves the stateof-the-art performance.","PeriodicalId":423182,"journal":{"name":"2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663184","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: 9

A Unified Labeling Model for Open-Domain Aspect-Based Sentiment Analysis 面向开放域面向方面的情感分析的统一标注模型

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00035

Qian Ji, Xiang Lin, Yinghua Ma, Gongshen Liu, Shilin Wang

{"title":"A Unified Labeling Model for Open-Domain Aspect-Based Sentiment Analysis","authors":"Qian Ji, Xiang Lin, Yinghua Ma, Gongshen Liu, Shilin Wang","doi":"10.1109/DSC50466.2020.00035","DOIUrl":"https://doi.org/10.1109/DSC50466.2020.00035","url":null,"abstract":"Aspect-based sentiment analysis involves aspect term extraction and sentiment prediction towards aspect terms. Recently, more researchers have proposed integrated approaches to accomplish two tasks simultaneously. However, such approaches always limit the domain, quantity, length and category of aspect terms, which greatly restricts its use. This paper aims to model the joint task as an extension of sequence labeling and presents a novel unified labeling model that supports a wide range of aspect terms. Unlike a conventional tagging scheme that predicts the boundary of an aspect term and classifies its sentiment step by step, our proposed model deals with two tasks simultaneously through one set of labels. Sentiment polarities are labeled directly on aspect term tokens, thus combining the boundary information with sentiment polarity in this unified tagging scheme. In this paper, we take Bidirectional Encoder Representations from Transformer (BERT) as the first representation layer to capture contextual features of the entire sentence. Conditional Random Field (CRF) follows BERT for minimizing empirical risk and labeling each token representation within given label sets based on the learned transition matrix. In our experiments, the proposed method demonstrates superior performance against multiple baselines on three benchmark datasets and one Twitter datasets collected by ourselves containing open-domain sentences and aspect terms with various categories, lengths and quantities.","PeriodicalId":423182,"journal":{"name":"2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133724202","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: 3

Word Level Domain-Diversity Attention Based LSTM Model for Sentiment Classification 基于词级领域多样性关注的LSTM情感分类模型

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00032

Haoliang Zhang, Hongbo Xu, Jinqiao Shi, Tingwen Liu, Chun Liao

{"title":"Word Level Domain-Diversity Attention Based LSTM Model for Sentiment Classification","authors":"Haoliang Zhang, Hongbo Xu, Jinqiao Shi, Tingwen Liu, Chun Liao","doi":"10.1109/DSC50466.2020.00032","DOIUrl":"https://doi.org/10.1109/DSC50466.2020.00032","url":null,"abstract":"Sentiment classification is an important task in Natural Language Processing research and it has considerable application significance. The complexity of human sentimental opinion implies that the hidden information such as application scenes or domains that behind the text may play an important role in the prediction of sentiment polarity. This paper presents a novel model for Sentiment Classification, Domain-Diversity Attention Mechanism based LSTM Model (DDAM-LSTM), integrating word level domain relevant features into an input side attention mechanism of LSTM model. Firstly, we propose a representing and calculating method of domain relevant features for each word according to its context. Then we find that the common words and certain domain-specific words show obvious different distribution states as for domain tendency. On this basis, an attention mechanism is designed to assign scale weights to the words at the input side of LSTM network according to their diversity of domain tendency. By combining this unique attention mechanism with the LSTM model, we achieve the goal of fusing the implied domain knowledge with the Neural Network. Experimental results on three public benchmark datasets show that our proposed model yields obvious performance improvement.","PeriodicalId":423182,"journal":{"name":"2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128315309","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 Survey of Depth Estimation Based on Computer Vision 基于计算机视觉的深度估计研究综述

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) Pub Date : 2020-07-01 DOI: 10.1109/DSC50466.2020.00028

Yang Liu, Jie Jiang, Jiahao Sun, L. Bai, Qi Wang

引用次数: 8