Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)最新文献_第3页

Stream computing for large-scale, multi-channel cyber threat analytics 用于大规模、多渠道网络威胁分析的流计算

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051865

D. Schales, Mihai Christodorescu, Xin Hu, Jiyong Jang, J. Rao, R. Sailer, M. Stoecklin, W. Venema, Ting Wang

{"title":"Stream computing for large-scale, multi-channel cyber threat analytics","authors":"D. Schales, Mihai Christodorescu, Xin Hu, Jiyong Jang, J. Rao, R. Sailer, M. Stoecklin, W. Venema, Ting Wang","doi":"10.1109/IRI.2014.7051865","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051865","url":null,"abstract":"The cyber threat landscape, controlled by organized crime and nation states, is evolving rapidly towards evasive, multi-channel attacks, as impressively shown by malicious operations such as GhostNet, Aurora, Stuxnet, Night Dragon, or APT1. As threats blend across diverse data channels, their detection requires scalable distributed monitoring and cross-correlation with a substantial amount of contextual information. With threats evolving more rapidly, the classical defense life cycle of post-mortem detection, analysis, and signature creation becomes less effective. In this paper, we present a highly-scalable, dynamic cybersecurity analytics platform extensible at runtime. It is specifically designed and implemented to deliver generic capabilities as a basis for future cybersecurity analytics that effectively detect threats across multiple data channels while recording relevant context information, and that support automated learning and mining for new and evolving malware behaviors. Our implementation is based on stream computing middleware that has proven high scalability, and that enables cross-correlation and analysis of millions of events per second with millisecond latency. We report the lessons we have learned from applying stream computing to monitoring malicious activity across multiple data channels (e.g., DNS, NetFlow, ARP, DHCP, HTTP) in a production network of about fifteen thousand nodes.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126500593","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

Automatic evaluation of medical doctors' performances while using a cricothyrotomy simulator 使用环甲软骨切开术模拟器时医生表现的自动评估

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051932

D. D’Auria, Fabio Persia

引用次数: 17

Towards model driven crowdsourcing: First experiments, methodology and transformation 走向模型驱动的众包:首次实验、方法论和转型

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051892

S. Vale

引用次数: 1

An ensemble classification approach for handling spatio-temporal drifts in partially labeled data streams 一种处理部分标记数据流时空漂移的集成分类方法

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051961

Tegjyot Singh Sethi, M. Kantardzic, Elaheh Arabmakki, Hanqing Hu

{"title":"An ensemble classification approach for handling spatio-temporal drifts in partially labeled data streams","authors":"Tegjyot Singh Sethi, M. Kantardzic, Elaheh Arabmakki, Hanqing Hu","doi":"10.1109/IRI.2014.7051961","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051961","url":null,"abstract":"The classification of streaming data requires learning in an environment where the distribution of the incoming data might change continuously. Stream classification methodologies need to adapt to these changes under limitations of time and memory resources. As such, it is not possible to expect all the samples in the stream to be labeled, as labeling is often time consuming and expensive. In this paper a new ensemble classification approach is proposed, which can handle Spatio-Temporal drifts in streams even when the labeling is limited. The proposed methodology uses a grid density clustering approach to track drifts in the spatial configuration of the data, and maintains a set of classifier models local to each cluster, to track its evolution over time. Structured weighted aggregation of the models across all clusters is performed to produce an overall effective prediction on a new sample. Additionally, a uniform sampling approach amenable to the grid representation of the clusters is proposed, which selects samples to be labeled while preserving the grid density information of the stream. This provides for better selection of representative samples to be labeled, for improved drift detection and handling, while maintaining the labeling budget. Experimental comparison with state of the art drift handling systems shows that the proposed methodology is able to give a high classification performance, with a manageable ensemble size and with only 10% of the samples labeled.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"447 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100835","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: 10

Advancing WSN physical security adopting TPM-based architectures 采用tpm架构推进WSN物理安全

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051916

M. Barbareschi, Ermanno Battista, A. Mazzeo, S. Venkatesan

引用次数: 12

A peer-to-peer network architecture for emerging applications 用于新兴应用程序的点对点网络体系结构

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051933

Khondkar R. Islam

引用次数: 0

Generating textual storyline to improve situation awareness in disaster management 生成文本故事情节，以提高灾害管理中的情况意识

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051942

Wubai Zhou, Chao Shen, Tao Li, Shu‐Ching Chen, Ning Xie

{"title":"Generating textual storyline to improve situation awareness in disaster management","authors":"Wubai Zhou, Chao Shen, Tao Li, Shu‐Ching Chen, Ning Xie","doi":"10.1109/IRI.2014.7051942","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051942","url":null,"abstract":"Hurricane Sandy affected the east coast of U.S. in 2012 and posed immense threats to businesses, human lives and properties. In order to minimize the consequent loss of a catastrophe like this, a critical task in disaster management is to understand situation updates about the disaster from a large number of disaster-related documents, and obtain a big picture of the disaster's trends and how it affects different areas. In this paper, we present a two-layer storyline generation framework which generates an overall or a global storyline of the disaster events in the first layer, and provides condensed information about specific regions affected by the disaster (i.e., a location-specific storyline) in the second layer. To generate the overall storyline of a disaster, we consider both temporal and spatial factors, which are encoded using integer linear programming. While for location-specific storylines, we employ a Steiner tree based method. Compared with the previous work of storyline generation, which generates flat storylines without considering spatial information, our framework is more suitable for large-scale disaster events. We further demonstrate the efficacy of our proposed framework through the evaluation on the datasets of three major hurricane disasters.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115120977","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: 24

Uncertainty reasoning for the "big data" semantic web “大数据”语义网的不确定性推理

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051884

Loukia Karanikola, Isambo Karali, S. McClean

引用次数: 6

SOA-GovMM: A meta model for a comprehensive SOA governance repository SOA- govmm:用于综合SOA治理存储库的元模型

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051889

Jan Konigsberger, S. Silcher, B. Mitschang

{"title":"SOA-GovMM: A meta model for a comprehensive SOA governance repository","authors":"Jan Konigsberger, S. Silcher, B. Mitschang","doi":"10.1109/IRI.2014.7051889","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051889","url":null,"abstract":"In recent years, the paradigm of service-oriented architecture (SOA) has more and more found its way into many organizations. The SOA principles of loosely coupled and reusable services has convinced decision makers in many organizations to start SOA initiatives. Yet, the lack of proper governance mechanisms has doomed many projects to fail. Although some SOA governance frameworks exist, they differ highly in scope and none of them covers the whole spectrum necessary to properly govern a SOA. In this paper we identify and discuss eleven core areas the governance of a SOA has to cover in order to realize the intended benefit in flexibility and agility. We then analyze and evaluate existing SOA governance frameworks with regard to those requirements. Subsequently, we present a meta model composed of four parts: Service Provider, Service Consumer, Organizational Structure and Business Object. We show, that those four parts cover all requirements for a comprehensive SOA governance repository. This allows an organization to leverage the information integrated in the repository to better govern their SOA and therefore improve the chances of its success.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122677650","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: 10

A framework for comparing early warning systems across domains: A step toward a data-integrated public health EWS 跨领域预警系统比较框架:迈向数据综合公共卫生预警系统的一步

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014) Pub Date : 2014-08-01 DOI: 10.1109/IRI.2014.7051875

Henry M. Kim, M. Laskowski, S. Moghadas, Amirehsan Sajad, Maaz Asif

{"title":"A framework for comparing early warning systems across domains: A step toward a data-integrated public health EWS","authors":"Henry M. Kim, M. Laskowski, S. Moghadas, Amirehsan Sajad, Maaz Asif","doi":"10.1109/IRI.2014.7051875","DOIUrl":"https://doi.org/10.1109/IRI.2014.7051875","url":null,"abstract":"Early Warning Systems (EWS) are crucial tools for public health, providing time for agencies to devise and enact control and mitigation measures in the face of emerging health threats. EWS offer similar benefits to agencies that manage other domains such as natural disasters or financial markets. After surveying various EWS, we develop a novel framework for characterizing EWS across domains. Key to this framework is the characterization of an Early Warning System's domain and focal event; whether its aim is prediction, detection, or warning, whether its focus is model, systems, or infrastructure; the extent of human intervention required; and its input data. We believe this framework is quite novel, but more importantly, it serves as a reference to chart future projects. We use it to verify that an opportunity exists in developing public health EWS that integrate a spectrum of inputs from Web 2.0 and social media data, data from sensors, data from, say, electronic health records, as well as human opinions and behaviors.","PeriodicalId":360013,"journal":{"name":"Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359775","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