2010 IEEE International Conference on Intelligence and Security Informatics最新文献_第3页

An executive decision support system for longitudinal statistical analysis of crime and law enforcement performance crime analysis system pacific region (CASPR) 犯罪纵向统计分析行政决策支援系统及执法表现太平洋地区犯罪分析系统(CASPR)

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484784

A. Ghaseminejad, P. Brantingham

引用次数: 5

The U.S. and the EU differences in anti-terrorism efforts 美国和欧盟在反恐方面存在分歧

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484777

M. Gallagher

引用次数: 0

Automatic construction of domain theory for attack planning 攻击规划领域理论的自动构建

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484775

Xiaochen Li, W. Mao, D. Zeng, Fei-Yue Wang

引用次数: 18

Towards effective improvement of the Bayesian Belief Network Structure learning 对贝叶斯信念网络结构学习的有效改进

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484745

Yan Tang, K. Cooper, João W. Cangussu, Kun Tian, Yin Wu

引用次数: 3

Generalizing terrorist social networks with K-nearest neighbor and edge betweeness for social network integration and privacy preservation 基于k近邻和边缘间性的恐怖分子社交网络泛化研究

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484776

Xuning Tang, Christopher C. Yang

引用次数: 17

Inter-Domain Routing Validator Based Spoofing Defence System 基于域间路由验证器的欺骗防御系统

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484754

Lei Wang, Tianbing Xia, J. Seberry

引用次数: 4

Predicting social ties in mobile phone networks 预测移动电话网络中的社会关系

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484780

Huiqi Zhang, R. Dantu

{"title":"Predicting social ties in mobile phone networks","authors":"Huiqi Zhang, R. Dantu","doi":"10.1109/ISI.2010.5484780","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484780","url":null,"abstract":"A social network dynamically changes since the social relationships (social ties) change over time. The evolution of a social network mainly depends on the evolution of the social relationships. The social-tie strengths of person-to-person are different one another even though they are in the same group. In this paper we investigate the evolution of person-to-person social relationships, quantify and predict social tie strengths based on call-detail records of mobile phones. We propose an affinity model for quantifying social-tie strengths in which a reciprocity index is integrated to measure the level of reciprocity between users and their communication partners. Since human social relationships change over time, we map the call-log data to time series of the social-tie strengths by the affinity model. Then we use ARIMA model to predict social-tie strengths. For validation of our results, we used actual call logs of 81 users collected for a period of 8 months at MIT by the Reality Mining Project group and also used call logs of 20 users collected for a period of 6 months by UNT's Network Security team. These users have around 5000 communication partners. The experimental results show that our model is effective. We achieve prediction performance with accuracy of average 95.2% for socially close and near members. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), and marketing.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126344972","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: 39

Cross Entropy approach for patrol route planning in dynamic environments 动态环境下巡逻路线规划的交叉熵方法

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484767

Xu Chen, T. Yum

引用次数: 13

Agent based correlation model for intrusion detection alerts 基于Agent的入侵检测告警关联模型

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484771

Ayman E. Taha, I. A. Ghaffar, A. Eldin, Hani M. K. Mahdi

{"title":"Agent based correlation model for intrusion detection alerts","authors":"Ayman E. Taha, I. A. Ghaffar, A. Eldin, Hani M. K. Mahdi","doi":"10.1109/ISI.2010.5484771","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484771","url":null,"abstract":"Alert correlation is a promising technique in intrusion detection. It analyzes the alerts from one or more intrusion detection system and provides a compact summarized report and high-level view of attempted intrusions which highly improves security effectiveness. Correlation component is a procedure which aggregates alerts according to certain criteria. The aggregated alerts could have common features or represent steps of pre-defined scenario attacks. Correlation approaches composed of a single component or a comprehensive set of components. The effectiveness of a component depends heavily on the nature of the dataset analyzed. The order of correlation component will affect the correlation process performance. Moreover not all components should be used for different dataset. This paper presents an agent-based alert correlation model. Learning agent learns the nature of dataset within a network then guides the whole correlation process and components in such a suitable way of which components could be used and in which order. The model improves the performance of correlation process by selecting the proper components to be used. This model assures minimum alerts to be processed on each component depending on the dataset and minimum time for correlation process.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116876058","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: 23

Identifying vulgar content in eMule network through text classification 通过文本分类识别eMule网络中的低俗内容

2010 IEEE International Conference on Intelligence and Security Informatics Pub Date : 2010-05-23 DOI: 10.1109/ISI.2010.5484751

Xiangtao Liu, Xueqi Cheng, Jingyuan Li, Haijun Zhai, Shuo Bai

引用次数: 1