2017 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献

The dynamics of health sentiments with competitive interactions in social media 健康情绪与社交媒体竞争互动的动态

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-08-18 DOI: 10.1109/ISI.2017.8004882

Saike He, Xiaolong Zheng, D. Zeng

{"title":"The dynamics of health sentiments with competitive interactions in social media","authors":"Saike He, Xiaolong Zheng, D. Zeng","doi":"10.1109/ISI.2017.8004882","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004882","url":null,"abstract":"Public sentiments affecting health outcomes are increasingly modulated by social media. Existing literature mainly focus on investigating how network structure affects the contagion of health sentiments. However, most of these studies neglect that the interaction topology change in time. In fact, the change of inter-individual connections over time is associated with individual attributes. The mechanism through which individual attributes reshapes the connection topology is mainly governed by the competition between two principles, i.e., homophily (establishing or reinforcing social connections) and homeostasis (preserving the total strength of social connections to each individual). No existing approaches are yet able to accommodate these two competing effects at the same time. We thus propose a new statistical model (H2 model, Homophily and Homestasis model) to depict the evolution of temporal network, which is governed by the competition of homophily and homeostasis. In addition, we consider the mediation effect of external shock events, which enables us to separate exogenous confounding factors. Evaluation on Twitter data suggests that H2 model can capture long-range sentiment dynamics and external shock events. In sentiment prediction, H2 consistently outperforms existing methods in terms of error rate. Through the model's shock tensor, we successfully detect several typical events, and reveal that users in negative emotions are more influenced by external shock events than those with positive emotions. Our findings have practical significance for those who supervise and guide health sentiments in online communities.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"8 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117329837","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

Phishing detection: A recent intelligent machine learning comparison based on models content and features 网络钓鱼检测:基于模型内容和特征的智能机器学习比较

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004877

Neda Abdelhamid, F. Thabtah, Hussein Abdel-jaber

{"title":"Phishing detection: A recent intelligent machine learning comparison based on models content and features","authors":"Neda Abdelhamid, F. Thabtah, Hussein Abdel-jaber","doi":"10.1109/ISI.2017.8004877","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004877","url":null,"abstract":"In the last decade, numerous fake websites have been developed on the World Wide Web to mimic trusted websites, with the aim of stealing financial assets from users and organizations. This form of online attack is called phishing, and it has cost the online community and the various stakeholders hundreds of million Dollars. Therefore, effective counter measures that can accurately detect phishing are needed. Machine learning (ML) is a popular tool for data analysis and recently has shown promising results in combating phishing when contrasted with classic anti-phishing approaches, including awareness workshops, visualization and legal solutions. This article investigates ML techniques applicability to detect phishing attacks and describes their pros and cons. In particular, different types of ML techniques have been investigated to reveal the suitable options that can serve as anti-phishing tools. More importantly, we experimentally compare large numbers of ML techniques on real phishing datasets and with respect to different metrics. The purpose of the comparison is to reveal the advantages and disadvantages of ML predictive models and to show their actual performance when it comes to phishing attacks. The experimental results show that Covering approach models are more appropriate as anti-phishing solutions, especially for novice users, because of their simple yet effective knowledge bases in addition to their good phishing detection rate.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115482631","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: 80

Alignment-free indexing-first-one hashing with bloom filter integration 无对齐索引第一哈希与布隆过滤器集成

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004878

Yenlung Lai, B. Goi, Tong-Yuen Chai

引用次数: 8

Reasoning crypto ransomware infection vectors with Bayesian networks 基于贝叶斯网络的加密勒索病毒感染向量推理

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004894

Aaron Zimba, Zhaoshun Wang, Hongsong Chen

引用次数: 14

Assessing medical device vulnerabilities on the Internet of Things 评估物联网上的医疗设备漏洞

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004903

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

引用次数: 38

Efficient parameter selection for SVM: The case of business intelligence categorization 支持向量机的有效参数选择:以商业智能分类为例

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004897

Hsin-Hsiung Huang, Zijing Wang, Wingyan Chung

{"title":"Efficient parameter selection for SVM: The case of business intelligence categorization","authors":"Hsin-Hsiung Huang, Zijing Wang, Wingyan Chung","doi":"10.1109/ISI.2017.8004897","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004897","url":null,"abstract":"Support Vector Machines (SVM) is a widely used technique for classifying high-dimensional data, especially in security and intelligence categorization. However, the performance of SVM can be adversely affected by poorly selected parameter values. Current approaches to SVM parameter selection mainly rely on extensive cross validation or anecdotal information, which can be inefficient and ineffective. In this research, we propose an efficient algorithm called Percentile-SVM (P-SVM) for selecting the parameter pair, (γ, C), of SVM with Gaussian kernels on metric data. P-SVM searches only a handful of percentiles of the squared Euclidean distances of data points to select the best pair of parameter values. To validate the algorithm, we applied P-SVM to categorizing business intelligence factors extracted from 6,859 sentences of 231 online news articles about four major companies in the information technology sector. The results show that P-SVM achieved a significant improvement in precision, recall, F-measure, and AUC over the LibSVM package (with default parameter values) used in WEKA, a widely used data mining software. These findings provide useful implication for relevant research and security informatics applications.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134032797","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 framework for digital forensics analysis based on semantic role labeling 基于语义角色标注的数字取证分析框架

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004876

Ravi Barreira, V. Pinheiro, Vasco Furtado

引用次数: 4

Tie strength still matters: Investigating interaction patterns of Al-Qaeda network in terror operations 联系强度仍然很重要:调查基地组织网络在恐怖行动中的互动模式

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004871

Ze Li, Duoyong Sun, Kun Cai, Bo Li

{"title":"Tie strength still matters: Investigating interaction patterns of Al-Qaeda network in terror operations","authors":"Ze Li, Duoyong Sun, Kun Cai, Bo Li","doi":"10.1109/ISI.2017.8004871","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004871","url":null,"abstract":"Terror operations are carried out by a team of terrorists with their interaction and cooperation. Different tie strengths, such as acquaintances, friends, and family members, influence the construction and reconstruction of the operation team. Understanding the interaction patterns of tie strength might be useful not only for advancing our understanding of terror operations, but ultimately for providing more effective methods of countering terrorist violence. To this end, in this paper, we designed an operation-related social network, and analyzed the tie strength selection and cooperative reconnection in the terror operation networks. We proposed three hypotheses about the tie strengths and had the Al-Qaeda dataset investigated to test the hypotheses. Results demonstrate that the terrorist organization has some different interaction patterns against the ordinary social organizations and the terror operations share similar features with crime events in rational choice. Our analysis reveals that tie strength matters in terror operations and is essentially helpful in making counterterrorism strategies.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131171410","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

Recognizing military vehicles in social media images using deep learning 利用深度学习在社交媒体图像中识别军用车辆

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004875

Tuomo Hiippala

引用次数: 7

End-to-end encrypted traffic classification with one-dimensional convolution neural networks 基于一维卷积神经网络的端到端加密流分类

2017 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2017-07-22 DOI: 10.1109/ISI.2017.8004872

Wei Wang, Ming Zhu, Jinlin Wang, Xuewen Zeng, Zhongzhen Yang

{"title":"End-to-end encrypted traffic classification with one-dimensional convolution neural networks","authors":"Wei Wang, Ming Zhu, Jinlin Wang, Xuewen Zeng, Zhongzhen Yang","doi":"10.1109/ISI.2017.8004872","DOIUrl":"https://doi.org/10.1109/ISI.2017.8004872","url":null,"abstract":"Traffic classification plays an important and basic role in network management and cyberspace security. With the widespread use of encryption techniques in network applications, encrypted traffic has recently become a great challenge for the traditional traffic classification methods. In this paper we proposed an end-to-end encrypted traffic classification method with one-dimensional convolution neural networks. This method integrates feature extraction, feature selection and classifier into a unified end-to-end framework, intending to automatically learning nonlinear relationship between raw input and expected output. To the best of our knowledge, it is the first time to apply an end-to-end method to the encrypted traffic classification domain. The method is validated with the public ISCX VPN-nonVPN traffic dataset. Among all of the four experiments, with the best traffic representation and the fine-tuned model, 11 of 12 evaluation metrics of the experiment results outperform the state-of-the-art method, which indicates the effectiveness of the proposed method.","PeriodicalId":423696,"journal":{"name":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125231993","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: 468