2010 IEEE International Conference on Intelligence and Security Informatics最新文献

{"title":"HPC - privacy model for collaborative skyline processing","authors":"B. Y. L. Chan, V. Ng","doi":"10.1109/ISI.2010.5484743","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484743","url":null,"abstract":"In general, skyline query is defined as finding a set of interesting database objects, which are not dominated to one another objects. A typical example is to find the hotel that is cheap and close to the beach. Since the introduction of skyline operator by Borzsonyi et al into database community, there has been a number of research works evolving and related publications related in last decade. However, there is only a few of them working on distributed skyline processing in collaborative computing environments. None of them considered the issue of privacy enforcement. The problem is that server has to disclose the sub-skylines (the actual skyline points) without privacy protection. In this paper, we propose the Hierarchical Piecewise Curve (HPC) model to enforce privacy during collaborative skyline processing and the private information can be released in a hierarchically controllable manner. Firstly we develop the polynomial expressions of Piecewise Curve (PC) by Spline interpolation to approximate the actual sub-skyline points. Figure 1 graphically showed the approximation. With Spline function, PC in R knocks are defined as: equations where there is no intersection among all knocks and the corresponding Mean Square Error (MSE) is defined as: equations. Secondly, we define the operators for the PC. If we have two servers working on the skyline query, we may have two Curve, c1 and c2 with respective intervals as a ≤ x ≤ b and n ≤ x ≤ m. We observed that there are 3 categories of relationships. First, c1 totally dominates c2; Second, c1 and c2 are totally independent; Third, c1 partially dominates c2. In the experiments, we observe that increasing the order of the polynomial and/or the number of PC resulted in reduction of MSE. Moreover, we observed the performance dropped when number of object in database increased. Meanwhile, the performance of skyline processing by the HPC model with 10 servers and 20 servers were relatively static when the database size increased. The poor performance of traditional approach was bottlenecked at constructing the global database for computing the global skyline. In the contrary, HPC model enabled distributed sub-skyline processing. Although there was computation overhead for merging curves (by equation 13), it could take advantage of distributing skyline computation among servers. Technically, we demonstrated Piecewise Curves (PC) as an answer approximation to response to the skyline query instead of actual skyline points. From the preliminary experimental results, we observed that the performance of HPC model for skyline processing out performance the traditional approach in distributed and cooperative computing environments.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"40 9 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":"114137908","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}

{"title":"Natural Language Processing based on Semantic inferentialism for extracting crime information from text","authors":"V. Pinheiro, Vasco Furtado, T. Pequeno, Douglas Nogueira","doi":"10.1109/ISI.2010.5484783","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484783","url":null,"abstract":"This article describes an architecture for Information Extraction systems on the web, based on Natural Language Processing (NLP) and especially geared toward the exploration of information about crime. The main feature of the architecture is its NLP module, which is based on the Semantic Inferential Model. We demonstrate the feasibility of the architecture through the implementation thereof to provide input for a collaborative web-based system of registering crimes called WikiCrimes.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"55 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":"132985955","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}

{"title":"Estimating sentiment orientation in social media for intelligence monitoring and analysis","authors":"R. Colbaugh, K. Glass","doi":"10.1109/ISI.2010.5484760","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484760","url":null,"abstract":"This paper presents a computational approach to inferring the sentiment orientation of “social media” content (e.g., blog posts) which focuses on the challenges associated with Web-based analysis. The proposed methodology formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and uses this framework to develop a semi-supervised sentiment classifier that is well-suited for social media domains. In particular, the proposed algorithm is capable of combining prior knowledge regarding the sentiment orientation of a few documents and words with information present in unlabeled data, which is abundant online. We demonstrate the utility of the approach by showing it outperforms several standard methods for the task of inferring the sentiment of online movie reviews, and illustrate its potential for security informatics through a case study involving the estimation of Indonesian public sentiment regarding the July 2009 Jakarta hotel bombings.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"56 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":"126523344","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}

{"title":"Automated sensing and social network analysis in virtual worlds","authors":"L. Overbey, Gregory McKoy, Jesse Gordon, Shannon McKitrick","doi":"10.1109/ISI.2010.5484741","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484741","url":null,"abstract":"In the last decade, social network analysis (SNA) tools have gained considerable interest in intelligence and security communities, as terrorist networks have become more global, decentralized, and flexible. Additionally, recent concerns have been voiced that virtual worlds provide likely breeding grounds for terrorism recruitment, communication, and coordination activities. This paper outlines research involving a survey of the approaches criminal or terrorist groups can take to covertly advance their causes in virtual environments. In addition, a methodology for collecting, storing, and analyzing information about virtual world cyber-behaviors is presented. As an initial effort, data collection devices were created in the virtual world Second Life (SL). These devices are capable of recording a variety of behavioral data about avatars in SL, and then piping that information to an external database. Information from the database can then be analyzed using SNA. Preliminary results indicate that a combined approach utilizing manual and automated intelligence techniques along with SNA provide valuable insights into the structure, function, and key players within virtual world social networks.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"45 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":"116900438","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}

{"title":"Automatically identifying the sources of large Internet events","authors":"K. Glass, R. Colbaugh, M. Planck","doi":"10.1109/ISI.2010.5484766","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484766","url":null,"abstract":"The Internet occasionally experiences large disruptions, arising from both natural and manmade disturbances, and it is of significant interest to develop methods for locating within the network the source of a given disruption (i.e., the network element(s) whose perturbation initiated the event). This paper presents a near real-time approach to realizing this logical localization objective. The proposed methodology consists of three steps: 1.) data acquisition/preprocessing, in which publicly available measurements of Internet activity are acquired, “cleaned”, and assembled into a format suitable for computational analysis, 2.) event characterization via tensor factorization-based time series analysis, and 3.) localization of the source of the disruption through graph theoretic analysis. This procedure provides a principled, automated approach to identifying the root causes of network disruptions at “whole-Internet” scale. The considerable potential of the proposed analytic method is illustrated through a computer simulation study and empirical analysis of a recent, large-scale Internet disruption.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"84 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":"132829104","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}

{"title":"Combining System Dynamics and Bayesian Belief Networks for Socio-Technical Risk Analysis","authors":"Z. Mohaghegh","doi":"10.1109/ISI.2010.5484736","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484736","url":null,"abstract":"In recent years, interdisciplinary methods integrating deterministic and probabilistic approaches have been gaining popularity due to their effectiveness in decision making for the design and operation of socio-technical systems. This paper demonstrates the value of combining the Bayesian Belief Networks (BBN) and System Dynamics (SD) for socio-technical predictive modeling. BBN is a technique for depicting probabilistic relations among elements of the model, where objective data are lacking and use of expert opinion is necessary. This is beneficial for the quantification of socio-technical models, dealing with the soft nature of human and organizational factors, however, BBN is inadequate for capturing dynamic aspects including feedback loops and delays. Combining SD with BBN can compensate for these BBN deficiencies. As an application, SD-BBN methodology is integrated with classical Probabilistic Risk Analysis (PRA) techniques in order to enable the Socio- Technical Risk Analysis framework to capture dynamic interactions of causal factors within their ranges of uncertainty.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"13 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":"114015349","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}

{"title":"Network neighborhood analysis","authors":"Michael D. Porter, Ryan Smith","doi":"10.1109/ISI.2010.5484781","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484781","url":null,"abstract":"We present a technique to represent the structure of large social networks through ego-centered network neighborhoods. This provides a local view of the network, focusing on the vertices and their kth order neighborhoods allowing discovery of interesting patterns and features of the network that would be hidden in a global network analysis. We present several examples from a corporate phone call network revealing the ability of our methods to discover interesting network behavior that is only available at the local level. In addition, we present an approach to use these concepts to identify abrupt or subtle anomalies in dynamic networks.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"70 3 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":"121989623","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}

{"title":"Workshop on current issues in predictive approaches to intelligence and security analytics","authors":"A. Sanfilippo","doi":"10.1109/ISI.2010.5484740","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484740","url":null,"abstract":"The increasing asymmetric nature of threats to the security, health and sustainable growth of our society requires that anticipatory reasoning become an everyday activity. Currently, the use of anticipatory reasoning is hindered by the lack of systematic methods for combining knowledge- and evidence-based models, integrating modeling algorithms, and assessing model validity, accuracy and utility. The workshop addresses these gaps with the intent of fostering the creation of a community of interest on model integration and evaluation that may serve as an aggregation point for existing efforts and a launch pad for new approaches.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"6 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":"124837868","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}

{"title":"BI -Intelligence for the business of crime fighting","authors":"J. Warden","doi":"10.1109/ISI.2010.5484752","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484752","url":null,"abstract":"Business Intelligence (BI) is the technology for collecting, collating, reporting and disseminating the timely and accurate intelligence to support decision-making. In policing, that decision-making is about preventing and reducing crime. The Edmonton Police Service uses BI to create a focus for the rapid deployment of resources to fight crime - right now!","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"58 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":"125094173","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}

{"title":"Breadth-depth triangulation for validation of modeling and simulation of complex systems","authors":"William N. Reynolds","doi":"10.1109/ISI.2010.5484739","DOIUrl":"https://doi.org/10.1109/ISI.2010.5484739","url":null,"abstract":"We give an approach for supporting validation for complex modeling and simulation. We present a means of characterizing methods, Breadth-Depth, which encompasses techniques ranging from simulation to unstructured human judgment. Based on this framework, we argue that a cost-effective method V&V of complex simulations is triangulation of simulation against breadth methods. We propose a specific instantiation of this approach, based on morphological analysis (MA). We describe a software tool to support MA-based breadth-depth triangulation.","PeriodicalId":434501,"journal":{"name":"2010 IEEE International Conference on Intelligence and Security Informatics","volume":"7 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":"125516042","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}