2019 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献_第4页

Study on rigidity, flexibility, resilience and tenacity of anti-interference ability of wireless communication system 无线通信系统抗干扰能力的刚性、柔性、弹性和韧性研究

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823305

Yingtao Niu, Cheng Li, Yan Liu, Yusheng Li

引用次数: 0

Quantum-Inspired Density Matrix Encoder for Sexual Harassment Personal Stories Classification 性骚扰个人故事分类的量子启发密度矩阵编码器

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823281

Peng Yan, Linjing Li, Weiyun Chen, D. Zeng

引用次数: 11

Research on Information Dissemination of Public Health Events Based on WeChat: A Case Study of Avian Influenza 基于微信的公共卫生事件信息传播研究——以禽流感为例

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823173

Tianyi Luo, Zhidong Cao, D. Zeng

引用次数: 2

A Prior Knowledge Based Neural Attention Model for Opioid Topic Identification 基于先验知识的阿片主题识别神经注意模型

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823280

Riheng Yao, Qiudan Li, W. Lo‐Ciganic, D. Zeng

引用次数: 0

Identifying High-Impact Opioid Products and Key Sellers in Dark Net Marketplaces: An Interpretable Text Analytics Approach 在暗网市场中识别高影响力阿片类产品和关键卖家:可解释的文本分析方法

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823196

Po-Yi Du, Mohammadreza Ebrahimi, Ning Zhang, Hsinchun Chen, Randall A. Brown, S. Samtani

{"title":"Identifying High-Impact Opioid Products and Key Sellers in Dark Net Marketplaces: An Interpretable Text Analytics Approach","authors":"Po-Yi Du, Mohammadreza Ebrahimi, Ning Zhang, Hsinchun Chen, Randall A. Brown, S. Samtani","doi":"10.1109/ISI.2019.8823196","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823196","url":null,"abstract":"As the Internet based applications become more and more ubiquitous, drug retailing on Dark Net Marketplaces (DNMs) has raised public health and law enforcement concerns due to its highly accessible and anonymous nature. To combat illegal drug transaction among DNMs, authorities often require agents to impersonate DNM customers in order to identify key actors within the community. This process can be costly in time and resource. Research in DNMs have been conducted to provide better understanding of DNM characteristics and drug sellers’ behavior. Built upon the existing work, researchers can further leverage predictive analytics techniques to take proactive measures and reduce the associated costs. To this end, we propose a systematic analytical approach to identify key opioid sellers in DNMs. Utilizing machine learning and text analysis, this research provides prediction of high-impact opioid products in two major DNMs. Through linking the high-impact products and their sellers, we then identify the key opioid sellers among the communities. This work intends to help law enforcement authorities to formulate strategies by providing specific targets within the DNMs and reduce the time and resources required for prosecuting and eliminating the criminals from the market.","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":"133668404","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

Targeted Addresses Identification for Bitcoin with Network Representation Learning 基于网络表示学习的比特币目标地址识别

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823249

Jiaqi Liang, Linjing Li, Weiyun Chen, D. Zeng

引用次数: 14

Attention Allocation of Twitter Users in Geopolitics 地缘政治中推特用户的注意力分配

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823342

Saike He, Changliang Li, Hailiang Wang, Xiaolong Zheng, Zhu Zhang, Jiaojiao Wang, D. Zeng

{"title":"Attention Allocation of Twitter Users in Geopolitics","authors":"Saike He, Changliang Li, Hailiang Wang, Xiaolong Zheng, Zhu Zhang, Jiaojiao Wang, D. Zeng","doi":"10.1109/ISI.2019.8823342","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823342","url":null,"abstract":"How people divide their attention across their friends can help to understand key issues in the realm of geopolitics. Such attention exploration allows us to compare people who focus a large portion of their attention on a small set of close friends with those disperse their attention more widely. Using 2.5 million twitter data written by 130 thousands users, we find the balance of attention is a relatively stable property of people across different modalities of interaction. It displays subtle variation across people with different characteristics and different modalities of interaction. Specifically, people’s attention is more focused in mention interactions, while those active in socialization tend to allocate higher portion of total attention to their close friends. Besides external interactions, people’s inner interests also affect their attention allocation. People spreading multiple memes tend to be focused, and those with more even distribution of memes are focused on their intimate friends. Finally, people’s relationships also plays an important role in their attention allocation. People are more likely to focus their attention on those most like them, and this similarity sequentially enhances the intimate relationship between them.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"72 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":"115959459","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

Application of Multi-domain Stereo Prevention and Control Technology in Counter-terrorism 多域立体防控技术在反恐中的应用

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823392

Yanfei Liu, Zhenhua Wang, Guangyu Zhang

引用次数: 1

NO-DOUBT: Attack Attribution Based On Threat Intelligence Reports 毫无疑问:攻击归因基于威胁情报报告

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823152

Lior Perry, Bracha Shapira, Rami Puzis

{"title":"NO-DOUBT: Attack Attribution Based On Threat Intelligence Reports","authors":"Lior Perry, Bracha Shapira, Rami Puzis","doi":"10.1109/ISI.2019.8823152","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823152","url":null,"abstract":"The task of attack attribution, i.e., identifying the entity responsible for an attack, is complicated and usually requires the involvement of an experienced security expert. Prior attempts to automate attack attribution apply various machine learning techniques on features extracted from the malware’s code and behavior in order to identify other similar malware whose authors are known. However, the same malware can be reused by multiple actors, and the actor who performed an attack using a malware might differ from the malware’s author. Moreover, information collected during an incident may contain many clues about the identity of the attacker in addition to the malware used. In this paper, we propose a method of attack attribution based on textual analysis of threat intelligence reports, using state of the art algorithms and models from the fields of machine learning and natural language processing (NLP). We have developed a new text representation algorithm which captures the context of the words and requires minimal feature engineering. Our approach relies on vector space representation of incident reports derived from a small collection of labeled reports and a large corpus of general security literature. Both datasets have been made available to the research community. Experimental results show that the proposed representation can attribute attacks more accurately than the baselines’ representations. In addition, we show how the proposed approach can be used to identify novel previously unseen threat actors and identify similarities between known threat actors.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"41 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":"114864985","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: 13

Social Cognition Construction of the Avian Flu based on Social Media Big Data 基于社交媒体大数据的禽流感社会认知构建

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823150

Yuejiao Wang, Zhidong Cao

引用次数: 0