The Experts below are selected from a list of 451863 Experts worldwide ranked by ideXlab platform
Ming Yang - One of the best experts on this subject based on the ideXlab platform.
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SMC - An interval centroid based spread spectrum watermark for tracing Multiple Network flows
2009 IEEE International Conference on Systems Man and Cybernetics, 2009Co-Authors: Xiaogang Wang, Junzhou Luo, Ming YangAbstract:Network flow watermarking schemes have been proposed to trace attackers in the presence of stepping stones or anonymized channels. Most existing interval-based watermarking schemes are ineffective at tracing Multiple Network flows in parallel due to their interference with each other, while recently proposed Direct Sequence Spread Spectrum (DSSS) watermarking technique is unsuitable for tracing low data rate traffic. By combining interval centroid based watermarking (ICBW) modulation approaches with spread spectrum (SS) based watermarking coding techniques, we herein propose an Interval Centroid Based Spread Spectrum Watermarking scheme (ICBSSW) for efficiently tracing Multiple Network flows in parallel. Based on our proposed theoretical model, a statistical analysis of ICBSSW, with no assumptions or limitations concerning the distribution of packet times, proves its effectiveness despite traffic timing perturbation and robustness against multi-flow attacks. The experiments using a large number of synthetically generated SSH traffic flows demonstrate that ICBSSW can efficiently trace Multiple flows simultaneously and achieve high secrecy by utilizing Multiple PN codes as random seeds for randomizing the location of the embedded watermark across Multiple flows.
Xiaogang Wang - One of the best experts on this subject based on the ideXlab platform.
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SMC - An interval centroid based spread spectrum watermark for tracing Multiple Network flows
2009 IEEE International Conference on Systems Man and Cybernetics, 2009Co-Authors: Xiaogang Wang, Junzhou Luo, Ming YangAbstract:Network flow watermarking schemes have been proposed to trace attackers in the presence of stepping stones or anonymized channels. Most existing interval-based watermarking schemes are ineffective at tracing Multiple Network flows in parallel due to their interference with each other, while recently proposed Direct Sequence Spread Spectrum (DSSS) watermarking technique is unsuitable for tracing low data rate traffic. By combining interval centroid based watermarking (ICBW) modulation approaches with spread spectrum (SS) based watermarking coding techniques, we herein propose an Interval Centroid Based Spread Spectrum Watermarking scheme (ICBSSW) for efficiently tracing Multiple Network flows in parallel. Based on our proposed theoretical model, a statistical analysis of ICBSSW, with no assumptions or limitations concerning the distribution of packet times, proves its effectiveness despite traffic timing perturbation and robustness against multi-flow attacks. The experiments using a large number of synthetically generated SSH traffic flows demonstrate that ICBSSW can efficiently trace Multiple flows simultaneously and achieve high secrecy by utilizing Multiple PN codes as random seeds for randomizing the location of the embedded watermark across Multiple flows.
Junzhou Luo - One of the best experts on this subject based on the ideXlab platform.
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SMC - An interval centroid based spread spectrum watermark for tracing Multiple Network flows
2009 IEEE International Conference on Systems Man and Cybernetics, 2009Co-Authors: Xiaogang Wang, Junzhou Luo, Ming YangAbstract:Network flow watermarking schemes have been proposed to trace attackers in the presence of stepping stones or anonymized channels. Most existing interval-based watermarking schemes are ineffective at tracing Multiple Network flows in parallel due to their interference with each other, while recently proposed Direct Sequence Spread Spectrum (DSSS) watermarking technique is unsuitable for tracing low data rate traffic. By combining interval centroid based watermarking (ICBW) modulation approaches with spread spectrum (SS) based watermarking coding techniques, we herein propose an Interval Centroid Based Spread Spectrum Watermarking scheme (ICBSSW) for efficiently tracing Multiple Network flows in parallel. Based on our proposed theoretical model, a statistical analysis of ICBSSW, with no assumptions or limitations concerning the distribution of packet times, proves its effectiveness despite traffic timing perturbation and robustness against multi-flow attacks. The experiments using a large number of synthetically generated SSH traffic flows demonstrate that ICBSSW can efficiently trace Multiple flows simultaneously and achieve high secrecy by utilizing Multiple PN codes as random seeds for randomizing the location of the embedded watermark across Multiple flows.
Nader Mohamed - One of the best experts on this subject based on the ideXlab platform.
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Self-configured Multiple-Network-interface socket
Journal of Network and Computer Applications, 2009Co-Authors: Nader Mohamed, Jameela Al-jaroodiAbstract:MuniSocket (Multiple-Network-Interface Socket) provides mechanisms to enhance the communication performance properties such as throughput, transfer time, and reliability by utilizing the existing Multiple-Network-interface cards on communicating hosts. Although the MuniSocket model has some communication performance advantages over the regular socket, it also has a number of usability and manageability drawbacks including the complexity of establishing Multiple channels and configuring them for good communication performance. This paper discusses some enhancements for MuniSocket using autonomic computing techniques. These techniques include self-discovery for discovering the existence of Network interfaces and their performance properties, self-configuration for establishing channels over the interfaces, and self-optimization for selecting the best channels combinations for efficiently sending messages of varying sizes. While these techniques enhance the communication performance among computers, they also reduce the complexity of configuring MuniSocket and make its interface compatible with the regular TCP socket interface, which in turn allows for a transparent use of MuniSocket by the applications.
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A Middleware-Level Parallel Transfer Technique over Multiple Network Interfaces
2003Co-Authors: Nader Mohamed, Jameela Al-jaroodi, Hong Jiang, David SwansonAbstract:Network middleware is a software layer that provides abstract Network APIs to hide the low-level technical details from users. Existing Network middleware support single Network interface and link (channel) message transfers. In this paper, we describe a middleware-level parallel transfer technique that utilizes Multiple Network interface units that may be connected through Multiple Networks. A prototype socket called MuniSocket (Multiple Network Interface Socket) has been implemented to provide this functionality. MuniSocket provides parallel message fragmentation and reconstruction mechanisms in addition to load balancing. It operates on any reliable transport protocol such as TCP and transparently provides an expandable high bandwidth solution that (1) reduces message transfer time, (2) provides fault tolerance, and (3) facilitates dynamic load balancing among the underlying Multiple Networks. The experimental evaluation of MuniSocket illustrates good performance gains, where a peak bandwidth of 187Mbps was achieved on two fast Ethernet Networks.
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COMPSAC (1) - Self-configuring communication middleware model for Multiple Network interfaces
29th Annual International Computer Software and Applications Conference (COMPSAC'05), 1Co-Authors: Nader MohamedAbstract:Communication middleware such as MuniCluster provides high-level communication mechanisms for Networked applications through hiding the low-level communication details from the applications. The MuniCluster model provides mechanisms to enhance the Network performance properties through message separations and parallel transfer. However, the configurations of such its services require various measurements and setups to efficiently utilize the availability of the Multiple Network interfaces. In this paper we introduce and evaluate a self-configuring model that allows applications to transparently utilize the existence of Multiple Network interfaces and Networks. Here we present enhancements to the MuniCluster model by adding the self-configuration mechanism. Using Network resource discovery and deciding on how to efficiently utilize the Multiple Networks, the model enhances overall communications performance. The proposed techniques deal with the heterogeneity of interfaces and Networks to enhance communication performance transparent form the applications.
Byung-jun Yoon - One of the best experts on this subject based on the ideXlab platform.
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Accurate Multiple Network alignment through context-sensitive random walk.
BMC systems biology, 2015Co-Authors: Hyundoo Jeong, Byung-jun YoonAbstract:Comparative Network analysis can provide an effective means of analyzing large-scale biological Networks and gaining novel insights into their structure and organization. Global Network alignment aims to predict the best overall mapping between a given set of biological Networks, thereby identifying important similarities as well as differences among the Networks. It has been shown that Network alignment methods can be used to detect pathways or Network modules that are conserved across different Networks. Until now, a number of Network alignment algorithms have been proposed based on different formulations and approaches, many of them focusing on pairwise alignment. In this work, we propose a novel Multiple Network alignment algorithm based on a context-sensitive random walk model. The random walker employed in the proposed algorithm switches between two different modes, namely, an individual walk on a single Network and a simultaneous walk on two Networks. The switching decision is made in a context-sensitive manner by examining the current neighborhood, which is effective for quantitatively estimating the degree of correspondence between nodes that belong to different Networks, in a manner that sensibly integrates node similarity and topological similarity. The resulting node correspondence scores are then used to predict the maximum expected accuracy (MEA) alignment of the given Networks. Performance evaluation based on synthetic Networks as well as real protein-protein interaction Networks shows that the proposed algorithm can construct more accurate Multiple Network alignments compared to other leading methods.
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Accurate Multiple Network alignment through context-sensitive random walk
BMC Systems Biology, 2015Co-Authors: Hyundoo Jeong, Byung-jun YoonAbstract:Background: Comparative Network analysis can provide an effective means of analyzing large-scale biological Networks and gaining novel insights into their structure and organization. Global Network alignment aims to predict the best overall mapping between a given set of biological Networks, thereby identifying important similarities as well as differences among the Networks. It has been shown that Network alignment methods can be used to detect pathways or Network modules that are conserved across different Networks. Until now, a number of Network alignment algorithms have been proposed based on different formulations and approaches, many of them focusing on pairwise alignment. Results: In this work, we propose a novel Multiple Network alignment algorithm based on a context-sensitive random walk model. The random walker employed in the proposed algorithm switches between two different modes, namely, an individual walk on a single Network and a simultaneous walk on two Networks. The switching decision is made in a context-sensitive manner by examining the current neighborhood, which is effective for quantitatively estimating the degree of correspondence between nodes that belong to different Networks, in a manner that sensibly integrates node similarity and topological similarity. The resulting node correspondence scores are then used to predict the maximum expected accuracy (MEA) alignment of the given Networks. Conclusions: Performance evaluation based on synthetic Networks as well as real protein-protein interaction Networks shows that the proposed algorithm can construct more accurate Multiple Network alignments compared to other leading methods.