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UNPCC: A novel unsupervised classification scheme for network intrusion detection

UNPCC: A novel unsupervised classification scheme for networ...

引用

18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006

作者： Xie, Zongxing Quirino, Thiago Shyu, Mei-Ling Chen, Shu-Ching Chang, LiWu Department of Electrical and Computer Engineering University of Miami Coral Gables FL 33124 United States Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL United States Center for High Assurance Computer Systems Naval Research Laboratory Washington DC United States

ISBN: (纸本)0769527280

The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (Unsu-pervised Principal Component Classifier) algorithm is a multi-class unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms. © 2006 IEEE.

关键词： Intrusion detection

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Collateral representative subspace projection modeling for supervised classification

Collateral representative subspace projection modeling for s...

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18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006

作者： Quirino, Thiago Xie, Zongxing Shyu, Mei-Ling Chen, Shu-Ching Chang, LiWu Department of Electrical and Computer Engineering University of Miami Coral Gables FL 33124 United States Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL 33199 United States Center for High Assurance Computer Systems Naval Research Laboratory Washington DC 20375 United States

ISBN: (纸本)0769527280

In this paper, a novel supervised classification approach called Collateral Representative Subspace Projection Modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, Decision Table (DT), Nearest Neighbor (NN), KNN, Support Vector Machine (SVM), 1-NN Best Warping Window DTW, 1-NN DTW with no Warping Window, and the well-known classifier boosting method AdaBoost with SVM. © 2006 IEEE.

关键词： Classification (of information)

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Comparison of Image Processing Resource Allocation for Multi-Target Tracking of Dismounted Targets

Comparison of Image Processing Resource Allocation for Multi...

引用

International Conference on Information Fusion

作者： Jon P. Champion Juan R. Vasquez Distributed Information Systems Branch Air Force Research Laboratory Rome NY USA Department of Electrical and Computer Engineering WPAFB Air Force Institute of Technology OH USA

ISBN: (纸本)1424409535

Dismounted targets can be tracked in urban environments with video sensors. Real-time systems are unable to process all of the imagery, demanding some method for prioritization of the processing resources. Furthermore, various segmentation algorithms exist within image processing, each algorithm possesses unique capabilities, and each algorithm has an associated computational cost. Additional complexity arises in the prioritization problem when targets become occluded (i.e., a building) and when the targets are intermixed with other dismounted entities. This added complexity leads to the question "which portions of the scene warrant both low cost and high cost processing?" The approach presented in this paper is to apply multi-target tracking techniques in conjunction with an integer programming optimization routine to determine optimal allocation of the video processing resources. This architecture results in feedback from the tracking routine to the image processing function which in turn enhances the ability of the tracker

关键词： Image processing Resource management Target tracking Costs Real time systems Image segmentation Computational efficiency Layout Linear programming Feedback

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The robot software communications architecture (RSCA): embedded middleware for networked service robots 06

The robot software communications architecture (RSCA): embed...

引用

International Symposium on parallel and distributed Processing (IPDPS)

作者： Seongsoo Hong Jaesoo Lee Hyeonsang Eom Gwangil Jeon Real-Time Operation Systems Laboratory School of Electrical Engineering and Computer Science Seoul National University of Technology Seoul South Korea Distributed Information Processing Laboratory School of Computer Science and Computer Engineering Seoul National University of Technology Seoul South Korea Department of Computer Engineering Korea Polytechnic University Siheung Gyeonggi South Korea

ISBN: (纸本)9781424400546

In this paper, we present a robot middleware technology named robot software communications architecture (RSCA) for its use in networked home service robots. The RSCA provides a standard operating environment for the robot applications together with a framework that expedites the development of such applications. The operating environment is comprised of a real-time operating system, a communication middleware, and a deployment middleware. Particularly, the deployment middleware supports the reconfiguration of component-based robot applications including installation, creation, start, stop, tear-down, and un-installation. In designing RSCA, we have adopted a middleware called SCA from the software defined radio domain and extend it since the original SCA lacks the real-time guarantees and appropriate event services. We have fully implemented RSCA and performed measurements to quantify its run-time performance. Our implementation clearly shows the viability of RSCA

关键词： Embedded software computer architecture Middleware Service robots Application software Standards development Real time systems Operating systems Software radio Performance evaluation

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A distributed agent-based approach to intrusion detection using the lightweight PCC anomaly detection classifier

A distributed agent-based approach to intrusion detection us...

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IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

作者： Zongxing Xie Thiago Quirino Mei-Ling Shyu Shu-Ching Chen LuWu Chang Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Distributed Multimedia Information System Laboratory Florida International University Miami FL USA Center for High Assurance Computer Systems Naval Research Laboratory Inc. Washington D.C. DC USA

ISBN: (纸本)0769525539;97

In this paper, a novel agent-based distributed intrusion detection system (IDS) is proposed, which integrates the desirable features provided by the distributed agent-based design methodology with the high accuracy and speed response of the principal component classifier (PCC). Experimental results have shown that the PCC lightweight anomaly detection classifier outperforms other existing anomaly detection algorithms such as the KNN and LOF classifiers. In order to assess the performance of the PCC classifier on a real network environment, the relative assumption model together with feature extraction techniques are used to generate normal and anomalous traffic in a LAN testbed. Finally, scalability and response performance of the proposed system are investigated through the simulation of the proposed communication architecture. The simulation results demonstrate a satisfactory linear relationship between the degradation of response performance and the scalability of the system

关键词： Intrusion detection Scalability Design methodology Detection algorithms Feature extraction Telecommunication traffic Traffic control Local area networks Testing Degradation

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Collateral Representative Subspace Projection Modeling for Supervised Classification

Collateral Representative Subspace Projection Modeling for S...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Thiago Quirino Zongxing Xie Mei-ling Shyu Shu-ching Chen Liwu Chang Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL USA Naval Research Laboratory Center for High Assurance Computer Systems Washington D.C. USA

In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, decision table (DT), nearest neighbor (NN), KNN, support vector machine (SVM), I-NN best warping window DTW, I-NN DTW with no warping window, and the well-known classifier boosting method AdaBoost with SVM

关键词： Principal component analysis Support vector machines Support vector machine classification Intrusion detection Training data Classification tree analysis Bayesian methods Laboratories distributed computing Power system modeling

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UNPCC: A Novel Unsupervised Classification Scheme for Network Intrusion Detection

UNPCC: A Novel Unsupervised Classification Scheme for Networ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Zongxing Xie Thiago Quirino Mei-ling Shyu Shu-ching Chen Liwu Chang Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL USA Center for High Assurance Computer Systems Naval Research Laboratory Washington D.C. DC USA

The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (unsupervised principal component classifier) algorithm is a multiclass unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms

关键词： Intrusion detection Classification algorithms Clustering algorithms computer networks distributed computing Laboratories Training data Traffic control Testing Machine learning algorithms

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An Integrated and Interactive Video Retrieval Framework with Hierarchical Learning Models and Semantic Clustering Strategy

An Integrated and Interactive Video Retrieval Framework with...

引用

IEEE International Conference on Information Reuse and Integration (IRI)

作者： Na Zhao Shu-ching Chen Mei-ling Shyu Stuart H. Rubin Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL USA Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Space and Naval Warfare Systems Command San Diego CA USA

In this research, we propose an integrated and interactive framework to manage and retrieve large scale video archives. The video data are modeled by a hierarchical learning mechanism called HMMM (hierarchical Markov model mediator) and indexed by an innovative semantic video database clustering strategy. The cumulated user feedbacks are reused to update the affinity relationships of the video objects as well as their initial state probabilities. Correspondingly, both the high level semantics and user perceptions are employed in the video clustering strategy. The clustered video database is capable of providing appealing multimedia experience to the users because the modeled multimedia database system can learn the user's preferences and interests interactively

关键词： Multimedia databases Hidden Markov models Information retrieval Multimedia systems Large scale integration Learning systems Clustering methods Clustering algorithms Feedback Content based retrieval

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An efficient distributed algorithm for constructing delay- and degree-bounded application-level multicast tree

An efficient distributed algorithm for constructing delay- a...

引用

8th International Symposium on parallel Architectures, Algorithms and Networks, I-SPAN 2005

作者： Feng, Liu Jingshu, Huang Xicheng, Lu Yuxing, Peng National Laboratory for Parallel and Distributed Processing Changsha 410073 China School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907-2035 United States

ISBN: (纸本)0769525091

In this paper we investigate the problem of finding a delay- and degree-bounded maximum sum of nodes application level multicast tree. We then proved the problem is NP-hard, and its relationship with the well-studied degree-bounded minimum maximum-delay multicast tree problem. We propose a distributed algorithm - OverStream, which adopts PPAF heuristic for father node to choose optimal child nodes based on multiple network parameters. Simulation results show that our algorithm is better than OMNI, SpreadIt, HMTP, and Zigzag in general. © 2005 IEEE.

关键词： Algorithms

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On design of cluster and grid computing environment toolkit for bioinformatics applications∗ 6th

引用

6th International Workshop on distributed Computing, IWDC 2004

作者： Yang, Chao-Tung Kuo, Yu-Lun Li, Kuan-Ching Gaudiot, Jean-Luc High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung40744 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung43301 Taiwan Parallel and Scalable Computer Architecture Laboratory Dept. of Electrical Engineering and Computer Science University of California – Irvine IrvineCA92697 United States

ISBN: (纸本)9783540240761

In this paper, we present BioGrid, a novel computing resource that combines advantages of grid computing technology with bioinformatics parallel applications. The grid environment permits the sharing of a large amount of idle computing resources and data. The investigation of bioinformatics demands orders of magnitude of computing resources and in parallel, the biological data are growing in such a high speed. By using BioGrid, we plan to integrate and share resources and biological data using the grid computing technology. Still in this paper, the well-known FASTA application is performed as matter of testing and analysis in our grid computing platform, as goal to show the improved efficiency and reduced computation time. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Bioinformatics

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