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2019 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献

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Marketing Pattern Risks Detection Based on Semi-Supervised Learning 基于半监督学习的营销模式风险检测
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823291
Qianyu Wang, Saike He, Xiaolong Zheng, D. Zeng
{"title":"Marketing Pattern Risks Detection Based on Semi-Supervised Learning","authors":"Qianyu Wang, Saike He, Xiaolong Zheng, D. Zeng","doi":"10.1109/ISI.2019.8823291","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823291","url":null,"abstract":"Detecting potential marketing pattern risks and preventing them can help enterprises lift operation efficiencies and reduce outlay costs. In this paper, we elaborate an ingenious method based on semi-supervised learning to identify latent marketing pattern risks for enterprises.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131807071","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
Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique 虚假推文的检测:使用网络分析和深度学习技术的实证调查
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823421
Jaewan Lim, Zhihui Liu, Lina Zhou
{"title":"Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique","authors":"Jaewan Lim, Zhihui Liu, Lina Zhou","doi":"10.1109/ISI.2019.8823421","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823421","url":null,"abstract":"Social media has become a powerful and efficient platform for information diffusion. The increasing pervasiveness of social media use, however, has brought about the problems of fraudulent accounts that are intended to diffuse misinformation or malicious contents. Twitter recently released comprehensive archives of fraudulent tweets that are possibly connected to a propaganda effort of Internet Research Agency (IRA) on the 2016 U.S. presidential election. To understand information diffusion in fraudulent networks, we analyze structural properties of the IRA retweet network, and develop deep neural network models to detect fraudulent tweets. The structure analysis reveals key characteristics of the fraudulent network. The experiment results demonstrate the superior performance of the deep learning technique to a traditional classification method in detecting fraudulent tweets. The findings have potential implications for curbing online misinformation.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125201600","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
Membership Detection for Real-world Groups Hidden in Social Network 隐藏在社交网络中的真实世界群体的成员检测
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823555
Jiale Liu, Yongzhong He
{"title":"Membership Detection for Real-world Groups Hidden in Social Network","authors":"Jiale Liu, Yongzhong He","doi":"10.1109/ISI.2019.8823555","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823555","url":null,"abstract":"Real-world groups are organizations or communities existed in the real world, such as the employees of a company, the students of a school, different from the virtual communities in social networks. The members of a real-world group may also appear in the social network and form into a virtual community. However, the community detection methods are not effective to detect the real-world groups because the members may lack interaction and sensitive attributes in the social network, so that the real-world groups appear to be hidden in the social network. This paper defines three kinds of real-world group models and defines sensitive attributes and sensitive relationships of users in real-world groups. We use random walk to detect memberships for real-world groups hidden in social network with no or little edges and sensitive attributes. We evaluate our model with a Facebook dataset. The experiments show that our model has an accuracy of 95%.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127523950","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
Understanding User Behaviors When Phishing Attacks Occur 了解钓鱼攻击发生时的用户行为
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823468
Yi Li, Kaiqi Xiong, Xiangyang Li
{"title":"Understanding User Behaviors When Phishing Attacks Occur","authors":"Yi Li, Kaiqi Xiong, Xiangyang Li","doi":"10.1109/ISI.2019.8823468","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823468","url":null,"abstract":"To study user security-related behaviors, we conduct an experimental study where participants take part in our experiments in a lab contained environment. We used a set of emails including phishing emails from the real world. We collect data including participants’ basic information and time measurement. We check whether or not factors such as intervention, phishing types, and incentive mechanisms play a major role in user behaviors when phishing attacks occur.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115328342","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
Automatic Tagging of Cyber Threat Intelligence Unstructured Data using Semantics Extraction 基于语义抽取的网络威胁情报非结构化数据自动标注
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823252
Tianyi Wang, Kam-pui Chow
{"title":"Automatic Tagging of Cyber Threat Intelligence Unstructured Data using Semantics Extraction","authors":"Tianyi Wang, Kam-pui Chow","doi":"10.1109/ISI.2019.8823252","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823252","url":null,"abstract":"Threat intelligence, information about potential or current attacks to an organization, is an important component in cyber security territory. As new threats consecutively occurring, cyber security professionals always keep an eye on the latest threat intelligence in order to continuously lower the security risks for their organizations. Cyber threat intelligence is usually conveyed by structured data like CVE entities and unstructured data like articles and reports. Structured data are always under certain patterns that can be easily analyzed, while unstructured data have more difficulties to find fixed patterns to analyze. There exists plenty of methods and algorithms on information extraction from structured data, but no current work is complete or suitable for semantics extraction upon unstructured cyber threat intelligence data. In this paper, we introduce an idea of automatic tagging applying JAPE feature within GATE framework to perform semantics extraction upon cyber threat intelligence unstructured data such as articles and reports. We extract token entities from each cyber threat intelligence article or report and evaluate the usefulness of them. A threat intelligence ontology then can be constructed with the useful entities extracted from related resources and provide convenience for professionals to find latest useful threat intelligence they need.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124721535","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
ISI 2019 Sponsors ISI 2019赞助商
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/isi.2019.8823215
{"title":"ISI 2019 Sponsors","authors":"","doi":"10.1109/isi.2019.8823215","DOIUrl":"https://doi.org/10.1109/isi.2019.8823215","url":null,"abstract":"","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123047795","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
Text Watermarking for OOXML Format Documents Based on Color Transformation 基于颜色变换的OOXML格式文档文本水印
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823497
Li Yang, Wenjie Guo, Yonggang Lu, Yi Yang, Lian Li, Zongli Liu
{"title":"Text Watermarking for OOXML Format Documents Based on Color Transformation","authors":"Li Yang, Wenjie Guo, Yonggang Lu, Yi Yang, Lian Li, Zongli Liu","doi":"10.1109/ISI.2019.8823497","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823497","url":null,"abstract":"A robust text watermarking approach has been proposed in this paper, in which the watermarks are embedded into the text by the transformation of characters’ RGB-style color in the OOXML format document. To get high watermark embedding capacity, two bits of the watermarking information are embedded between every two characters. The initial text content of the document is not changed after the embedding because of the good characteristics of OOXML format and the subtle adjustment of characters’ color, which ensure the good imperceptibility of the watermarking method. Most attacks such as “copy”, “save as”, “insert” and “delete” operations can be resisted by the proposed approach, and the location of the “insert” and “delete” operations can be detected by redundant embedding of the watermarking information. The proposed approach can be applied to files in “docx” and “doc” format.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126270549","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
Lawsuit category prediction based on machine learning 基于机器学习的诉讼类别预测
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823328
Yuru Xu, Mingming Zhang, Shaowu Wu, Junfeng Hu
{"title":"Lawsuit category prediction based on machine learning","authors":"Yuru Xu, Mingming Zhang, Shaowu Wu, Junfeng Hu","doi":"10.1109/ISI.2019.8823328","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823328","url":null,"abstract":"In this paper, based on the comprehensive information of companies, 612 characteristic parameters are extracted and mined, and two prediction models of the categories of lawsuits are established. The first model is the combinatorial prediction model, which transforms the classification problem into a single-category regression problem. After the Laplace Smoothing treatment of the training label, LightGBM model was used for the 5-fold cross-validation for each of the categories. The Top 1 and Top 2 accuracy of the final combined model was 40.868% and 21.826%, respectively. The second model is Artificial Neural Network (ANN) model, which directly treats the problem as a classification problem. The ANN model with five layers is used to classify and predict the categories of lawsuits, and its Top 1 accuracy is 40.803%, and Top 2 accuracy is 21.243%. Although the accuracy is not ideal, but the method is feasible and can be used for reference. Finally, this paper analyzes the categories of misclassified lawsuits in detail.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131852979","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
Forensic Analysis of Bitcoin Transactions 比特币交易的取证分析
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823498
Y. Wu, Anthony Luo, Dianxiang Xu
{"title":"Forensic Analysis of Bitcoin Transactions","authors":"Y. Wu, Anthony Luo, Dianxiang Xu","doi":"10.1109/ISI.2019.8823498","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823498","url":null,"abstract":"Bitcoin [1] as a popular digital currency has been a target of theft and other illegal activities. Key to the forensic investigation is to identify bitcoin addresses involved in bitcoin transfers. This paper presents a framework, FABT, for forensic analysis of bitcoin transactions by identifying suspicious bitcoin addresses. It formalizes the clues of a given case as transaction patterns defined over a comprehensive set of features. FABT converts the bitcoin transaction data into a formal model, called Bitcoin Transaction Net (BTN). The traverse of all bitcoin transactions in the order of their occurrences is captured by the firing sequence of all transitions in the BTN. We have applied FABT to identify suspicious addresses in the Mt.Gox case. A subgroup of the suspicious addresses has been found to share many characteristics about the received/transferred amount, number of transactions, and time intervals.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133281389","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
ISI 2019 Copyright Page ISI 2019版权页面
2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/isi.2019.8823388
{"title":"ISI 2019 Copyright Page","authors":"","doi":"10.1109/isi.2019.8823388","DOIUrl":"https://doi.org/10.1109/isi.2019.8823388","url":null,"abstract":"","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131636026","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
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