2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications最新文献_第5页

New search algorithm for randomly located objects: A non-cooperative agent based approach 随机定位对象的新搜索算法:一种基于非合作智能体的方法

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-08 DOI: 10.1109/CISDA.2009.5356564

D. Calitoiu

引用次数: 12

Multiple UAV teams for multiple tasks 多个无人机小组执行多个任务

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-08 DOI: 10.1109/CISDA.2009.5356535

P. Sujit, J. Sousa, F. Pereira

引用次数: 5

Information assurances and threat identification in networked organizations 网络组织中的信息保障与威胁识别

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-08 DOI: 10.1109/CISDA.2009.5356532

Terrill L. Frantz, Kathleen M. Carley

{"title":"Information assurances and threat identification in networked organizations","authors":"Terrill L. Frantz, Kathleen M. Carley","doi":"10.1109/CISDA.2009.5356532","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356532","url":null,"abstract":"We present a brief report on a controlled experiment that provides valuable statistics to network-oriented defence analysts involved in threat identification. These statistics estimate the accuracy of the top-central actor findings that have been derived from relational data classically found in real-world datasets, such as those collected on distributed, covert organizations. Our experiment involved cellular social-networks with four types of data error: missing links, missing actors, extra links, and extra actors. We provide statistical results for top threat identification from the perspective of four traditional measures of network centrality: degree, betweenness, closeness and eigenvector. The results from our experiment provide a statistical estimate of the accuracy of the top-1 and top-3 actors as indicated by the observed data. Using these statistics a quantitative indication of reliability can be provided along with defence intelligence estimates of covert-organization leadership derived from relational network data. We provide lookup tables for the specific situations created for this experiment, from which other conditions may be loosely estimated. This work has highly practical implications for operational analysts and consumers of such analyses, particularly in the terrorist network and drug-trafficking domains. This work also lays the groundwork for developing more intricate estimates of reliability for other network-related, analytic tasks of analysts — from more extensive key-actor identification tasks to assessing the statistical reliability of the centrality measures in and of themselves.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"24 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77827768","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: 4

mTRACK - Monitoring time-varying relations in approximately categorised knowledge 监测近似分类知识的时变关系

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-08 DOI: 10.1109/CISDA.2009.5356563

T. Martin, Yun Shen

引用次数: 6

Multisensor-multitarget tracking testbed 多传感器-多目标跟踪试验台

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-08 DOI: 10.1109/CISDA.2009.5356526

D. Akselrod, R. Tharmarasa, T. Kirubarajan, Z. Ding, T. Ponsford

引用次数: 5

Passive multitarget tracking using transmitters of opportunity 利用机会发射器进行被动多目标跟踪

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-08 DOI: 10.1109/CISDA.2009.5356536

R. Tharmarasa, T. Kirubarajan, M. McDonald

引用次数: 13

Bio-inspired computing for launch vehicle design and trajectory optimization 运载火箭设计与轨迹优化的仿生计算

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-08 DOI: 10.1109/CISDA.2009.5356548

S. Sundaram, Hai-Jun Rong, N. Sundararajan

引用次数: 5

A comparison of techniques for on-line incremental learning of HMM parameters in anomaly detection 异常检测中HMM参数在线增量学习技术的比较

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2009-07-01 DOI: 10.1109/CISDA.2009.5356542

Wael Khreich, Eric Granger, A. Miri, R. Sabourin

{"title":"A comparison of techniques for on-line incremental learning of HMM parameters in anomaly detection","authors":"Wael Khreich, Eric Granger, A. Miri, R. Sabourin","doi":"10.1109/CISDA.2009.5356542","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356542","url":null,"abstract":"Hidden Markov Models (HMMs) have been shown to provide a high level performance for detecting anomalies in intrusion detection systems. Since incomplete training data is always employed in practice, and environments being monitored are susceptible to changes, a system for anomaly detection should update its HMM parameters in response to new training data from the environment. Several techniques have been proposed in literature for on-line learning of HMM parameters. However, the theoretical convergence of these algorithms is based on an infinite stream of data for optimal performances. When learning sequences with a finite length, on-line incremental versions of these algorithms can improve discrimination by allowing for convergence over several training iterations. In this paper, the performance of these techniques is compared for learning new sequences of training data in host-based intrusion detection. The discrimination of HMMs trained with different techniques is assessed from data corresponding to sequences of system calls to the operating system kernel. In addition, the resource requirements are assessed through an analysis of time and memory complexity. Results suggest that the techniques for online incremental learning of HMM parameters can provide a higher level of discrimination than those for on-line learning, yet require significantly fewer resources than with batch training. On-line incremental learning techniques may provide a promising solution for adaptive intrusion detection systems.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"4 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79547493","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: 15

Joint path planning and sensor subset selection for multistatic sensor networks 多静态传感器网络的联合路径规划与传感器子集选择

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2008-04-03 DOI: 10.1117/12.779217

R. Tharmarasa, T. Kirubarajan, T. Lang

{"title":"Joint path planning and sensor subset selection for multistatic sensor networks","authors":"R. Tharmarasa, T. Kirubarajan, T. Lang","doi":"10.1117/12.779217","DOIUrl":"https://doi.org/10.1117/12.779217","url":null,"abstract":"Since inexpensive passive sensors have become available, it is possible to deploy a large number of them for tracking purposes in Anti-Submarine Warfare (ASW). However, modern submarines are quiet and difficult to track with passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition to passive receivers (e.g., sonobouys), have the potential to improve the tracking performance. The performance can be improved further by moving the transmitters according to existing target states and any possible new target states. Even though a large number of passive sensors are available, due to frequency, processing power and other physical limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the transmitters and select a subset from the available passive sensors in order to optimize the tracking performance. In this paper, the Posterior Crame´r-Rao Lower Bound (PCRLB), which gives a lower bound on estimation uncertainty, is used as the performance measure. An algorithm is presented to decide jointly the optimal path of the movable transmitters, by considering transmitters' operational constraints, and the optimal subset of passive sensors that should be used at each time steps for tracking multiple, possibly time-varying, number of targets. The effect of sensor location uncertainties, due to deployment error and possible sensor drifting, on the tracking performance is addressed in the sensor management algorithm. Simulation results illustrating the performance of the proposed algorithm are presented.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"3 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2008-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80859280","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: 15

A Template-based Method for Force Group Classification in Situation Assessment 态势评估中基于模板的部队群分类方法

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications Pub Date : 2007-01-01 DOI: 10.1109/CISDA.2007.368139

Huimin Chai, Baoshu Wang

引用次数: 0