2017 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献_第3页

Criminal intelligence surveillance and monitoring on social media: Cases of cyber-trafficking 社会媒体上的刑事情报监视与监控:网络贩卖案件

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004908

Wingyan Chung, Elizabeth Mustaine, D. Zeng

{"title":"Criminal intelligence surveillance and monitoring on social media: Cases of cyber-trafficking","authors":"Wingyan Chung, Elizabeth Mustaine, D. Zeng","doi":"10.1109/ISI.2017.8004908","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004908","url":null,"abstract":"Cyber-trafficking is the illegal transport of humans, drugs, weapons, or goods by means of Internet-enabled electronic devices. Currently, there is a lack of surveillance and understanding of the rapidly growing social concern about cyber-trafficking (CT). This paper describes the Cyber-Trafficking Surveillance System (CyTraSS) and provides preliminary findings of using the system to monitor CT social media discussions. CyTraSS supports flexible collection, analysis, and visualization of social media content, user linkage, and temporal features. The CyTraSS database contains a focused collection of over 2,318,691 social media messages posted by over 740,070 users who discussed about trafficking crimes and issues. CyTraSS supports keyword search, sentiment analysis, message statistics summarization, and influential leader identification. Emotion expressed in social media messages is extracted and aggregated quantitatively to indicate community mood. We examined three use cases about a sex trafficker identified by a flight attendant, Federal use of private prisons, and trafficking cases related to Beijing. These time-sensitive incidents are highly-relevant to CT and were identified by using clues provided by CyTraSS. The results have strong implications for understanding CT concern on social media.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131215032","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: 8

Topic and user based refinement for competitive perspective identification 基于主题和用户的竞争视角识别细化

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004888

Junjie Lin, W. Mao, D. Zeng

{"title":"Topic and user based refinement for competitive perspective identification","authors":"Junjie Lin, W. Mao, D. Zeng","doi":"10.1109/ISI.2017.8004888","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004888","url":null,"abstract":"The competitive perspective implied in online texts reflect people's conflicts in their stances and viewpoints. Competitive perspective identification aims to determine people's inclinations to one of multiple competitive perspectives, which is an important research issue and can facilitate many security-related applications. As the word usage of different perspectives is distinct in various topics, in this paper, we first proposes a supervised topic-refined method for competitive perspective identification. Our method refines perspective classifiers with the document-topic distributions mined from texts. To reduce human labor in data annotation, we further extend our work in a semi-supervised manner and propose a user-based bootstrapping framework. As the perspectives people hold are relatively stable, our bootstrapping process leverages the user-level perspective consistency to select high-quality classified texts from unlabeled corpus and boost the perspective classifier iteratively. Experimental studies show the effectiveness of our proposed approach in identifying the competitive perspectives of online texts.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114200172","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

Static detection of Android malware based on improved random forest algorithm 基于改进随机森林算法的Android恶意软件静态检测

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004913

Su Hou, Tianliang Lu, Yanhui Du, Jing Guo

引用次数: 5

Developing curricular modules for cybersecurity informatics: An active learning approach 开发网络安全信息学课程模块:一种主动学习方法

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004899

Wingyan Chung

引用次数: 4

Proactive information security behavior and individual creativity: Effects of group culture and decentralized IT governance 主动信息安全行为与个体创造力:群体文化与分散IT治理的影响

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004865

Canchu Lin, Jenell L. S. Wittmer

引用次数: 9

Prioritized active learning for malicious URL detection using weighted text-based features 优先主动学习恶意URL检测使用加权文本为基础的特征

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004883

S. Bhattacharjee, A. Talukder, E. Al-Shaer, Pratik Doshi

{"title":"Prioritized active learning for malicious URL detection using weighted text-based features","authors":"S. Bhattacharjee, A. Talukder, E. Al-Shaer, Pratik Doshi","doi":"10.1109/ISI.2017.8004883","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004883","url":null,"abstract":"Data analytics is being increasingly used in cyber-security problems, and found to be useful in cases where data volumes and heterogeneity make it cumbersome for manual assessment by security experts. In practical cyber-security scenarios involving data-driven analytics, obtaining data with annotations (i.e. ground-truth labels) is a challenging and known limiting factor for many supervised security analytics task. Significant portions of the large datasets typically remain unlabelled, as the task of annotation is extensively manual and requires a huge amount of expert intervention. In this paper, we propose an effective active learning approach that can efficiently address this limitation in a practical cyber-security problem of Phishing categorization, whereby we use a human-machine collaborative approach to design a semi-supervised solution. An initial classifier is learnt on a small amount of the annotated data which in an iterative manner, is then gradually updated by shortlisting only relevant samples from the large pool of unlabelled data that are most likely to influence the classifier performance fast. Prioritized Active Learning shows a significant promise to achieve faster convergence in terms of the classification performance in a batch learning framework, and thus requiring even lesser effort for human annotation. An useful feature weight update technique combined with active learning shows promising classification performance for categorizing Phishing/malicious URLs without requiring a large amount of annotated training samples to be available during training. In experiments with several collections of PhishMonger's Targeted Brand dataset, the proposed method shows significant improvement over the baseline by as much as 12%.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267772","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: 14

Attack pattern mining algorithm based on security log 基于安全日志的攻击模式挖掘算法

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004918

Keyi Li, Yang Li, Jianyi Liu, Ru Zhang, Xi Duan

引用次数: 6

Mapping users across social media platforms by integrating text and structure information 通过整合文本和结构信息来映射跨社交媒体平台的用户

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004884

Song Sun, Qiudan Li, Peng Yan, D. Zeng

{"title":"Mapping users across social media platforms by integrating text and structure information","authors":"Song Sun, Qiudan Li, Peng Yan, D. Zeng","doi":"10.1109/ISI.2017.8004884","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004884","url":null,"abstract":"With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone. However, text information and structure information reflect different aspects of a user. An organic combination of them is beneficial to mining user behavior patterns, thus help identify users across platforms accurately. The challenging problems are the effective representation and similarity computation of the text and structure information. We propose a mapping method which integrates text and structure information. At first, the model represents user name, description, location information based on word2vec or string matching, and friend information represented as relation network is regarded as structure information. Then these information are used for similarity computation using Jaccard index or cosine similarity. After similarity computation, a linear model is adopted to get the overall similarity of user pairs to perform user mapping. Based on the proposed method, we develop a prototype system, which allows users to set and adjust the weights of different information, or set expected index. The experimental results on a real-world dataset demonstrate the efficiency of the proposed model.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"26 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132060750","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: 7

Benchmarking vulnerability scanners: An experiment on SCADA devices and scientific instruments 对漏洞扫描器进行基准测试:对SCADA设备和科学仪器的实验

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004879

Malaka El, Emma McMahon, S. Samtani, Mark W. Patton, Hsinchun Chen

引用次数: 20

Exploring linguistic features for extremist texts detection (on the material of Russian-speaking illegal texts) 极端文本检测的语言特征探析(以俄语非法文本为例)

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004907

D. Devyatkin, I. Smirnov, Ananyeva Margarita, M. Kobozeva, Chepovskiy Andrey, Solovyev Fyodor

引用次数: 17