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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zikang Jin Changchun Yin Piji Li Lu Zhou Liming Fang Xiangmao Chang Zhe Liu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

Improving the transferability of adversarial examples for the purpose of attacking unknown black-box models has been intensively studied. In particular, feature-level transfer-based attacks, which destroy the intermediate feature outputs of source models, are proven to generate more transferable adversarial examples. However, existing state-of-the-art feature-level attacks only destroy a single intermediate layer, this severely limits the transferability of adversarial examples. And all of these attacks have a vague distinction between positive and negative features. By contrast, we propose the Multi-layer Feature Division Attack (MFDA), which aggregates multi-layer feature information on the basis of feature division to attack. Extensive experimental evaluation demonstrates that MFDA can significantly boost the adversarial transferability and quantitatively distinguish the effects of positive and negative features on transferability. Compared to the state-of-the-art feature-level attacks, our improvement methods with MFDA increase the average success rate by 2.8% against normally trained models and 3.0% against adversarially trained models.

关键词： Aggregates Closed box Signal processing Acoustics Speech processing

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Adversarial Attack and Defense for Transductive Support Vector Machine

Adversarial Attack and Defense for Transductive Support Vect...

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International Joint Conference on Neural Networks (IJCNN)

作者： Li Liu Haiyan Chen Changchun Yin Liming Fang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial Defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.

关键词： Training Support vector machines Accuracy Machine learning algorithms Neural networks Robustness Vectors

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EdgeAnchor: A Rapid and Balanced File Storage Strategy at the Network Edge

EdgeAnchor: A Rapid and Balanced File Storage Strategy at th...

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International Conference on parallel and Distributed Systems (ICPADS)

作者： Han Liu Xingrui Xie Zhuopu Zhang Geyao Cheng Lailong Luo Deke Guo Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers’ storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively.

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Clustering-preserving Network Flow Sketching

Clustering-preserving Network Flow Sketching

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yongquan Fu Dongsheng Li Siqi Shen Yiming Zhang Kai Chen Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Network monitoring is vital in modern clouds and data center networks that need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash *** paper presents a clustering-preserving sketch method to be resilient to hash collisions. We provide an equivalence analysis of the sketch in terms of the K-means clustering. Based on the analysis result, we cluster similar network flows to the same bucket array to reduce the estimation variance and use the average to obtain unbiased estimation. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 10 3 to 10 5 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%.

关键词： Monitoring Arrays Estimation error Adaptation models Approximation error Data centers

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Research on Online Failure Prediction Model and Status Pretreatment Method for Exascale System

Research on Online Failure Prediction Model and Status Pretr...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Hao Zhou Yanhuang Jiang National Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China

The reliability issue of Exascale system is extremely serious. Traditional passive fault-tolerant methods, such as rollback-recovery, can not fully guarantee system reliability any more because of their large executing overhead and long recovering duration. Active fault tolerance is expected to become another important fault-tolerant approach for Exascale system. Focusing on system failure prediction, which is one key step of active fault tolerance, we construct online failure prediction model and research on the effective method of system status pretreatment. In order to improve the accuracy and real-time feature of current methods, the proposed Improved Adaptive Semantic Filter (IASF) method processes the latest system logs regularly, filtering useless information out of them according to their semantics. Adopting the main idea of Vector Space Model (VSM), IASF method creates Event Vector corresponding to each log record. By calculating the cosine of vectorial angle, it evaluates the semantics correlation between different log records, and then executes temporal and spatial redundant filter considering the burst feature of log records. IASF method is insensitive to the type of system log and does not introduce any expert system or domain knowledge. The experiment result shows that system can eliminate about 99.6% of useless log records after executing IASF method.

关键词： Fault tolerance Fault tolerant systems Vectors Correlation Information filters Predictive models

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Skyline Query processing on Interval Uncertain Data

Skyline Query Processing on Interval Uncertain Data

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IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW)

作者： Xiaoyong Li Yijie Wang Xiaoling Li Guangdong Wang National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Many recent applications involve processing and analyzing uncertain data. Recently, several research efforts have addressed answering skyline queries efficiently on massive uncertain datasets. However, the research lacks methods to compute these queries on uncertain data, where each dimension of the uncertain object is represented as an interval or an exact value. In this paper, we extensively study the problem of skyline query on these interval based uncertain objects, which has never been studied before. We first model the problem of querying the skylines on interval datasets. Typically, we address two efficient algorithms with I/O optimal for the conventional interval skyline queries and constrained interval skyline queries, respectively. Extensive experiments demonstrate the efficiency of all our proposed algorithms.

关键词： Probability density function distribution functions Probability distribution Random variables Computational modeling Uncertainty Distributed databases

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Providing Responsiveness Requirement Based Consistency in DVE

Providing Responsiveness Requirement Based Consistency in DV...

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International Conference on parallel and Distributed Systems (ICPADS)

作者： Wei Zhang Hangjun Zhou Yuxing Peng Sikun Li National Laboratory of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Consistency and responsiveness are two important factors in providing the sense of reality in Distributed Virtual Environment (DVE). However, it is not easy to optimize both aspects because of the trade-off between these two factors. As a result, most existing consistency maintenance methods ignored the responsiveness requirements, or just assumed a simple responsiveness requirement model which cannot meet the real need of DVE systems. In this paper, we first present a new responsiveness requirement model. The model can describe requirement satisfaction situation of each node. Base on this model, we propose a responsiveness requirement based consistency method. The method can adjust the utilization of time resource according to the requirements of different nodes and improve the overall responsiveness performance by at least 20%. Therefore, it provides a good support to increase the applicability of DVE systems.

关键词： Delay effects Virtual environment Distributed processing Computer science Games Distance learning Electronic commerce Safety Research and development Real time systems

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A study of strategies to restrain the C&C activities of structured P2P botnets

A study of strategies to restrain the C&C activities of stru...

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International Conference on Computer sciences and Convergence Information technology (ICCIT)

作者： Tianzuo Wang Bo Liu Huaimin Wang Hao Ren Xiaolong Ma School of Computer Science National Laboratory for Parallel and Distributed Processing National University of Defense and Technology Changsha China

Botnets are threatening the Internet heavily, and more and more botnets are utilizing the P2P technology to build their C&C (Command and Control) mechanisms. Some research have been made to compare the resilience of structured P2P botnets and unstructured ones, against elimination of nodes, but the problem that which eliminating strategy is the best is rarely studied. In this paper, we proposed a new metric called the half point, to measure the effectiveness of different strategies. We also selected seven different eliminating strategies and compared them. Through extensive simulations, we find that RBC is the best eliminating strategy. Further analysis shows that for the strategy RBC, the average degree of nodes in the botnet have the most significant influence. The bigger the average degree is, the bigger the half point of RBC is, which implies that node eliminating may not be a reasonable choice for mitigating botnets with big average degree. Results of this paper can provide guidance for restraining structured P2P botnets.

关键词： C&C mechanism betweenness centrality coreness eliminating strategy half point restrain structured P2P botnets

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3DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generation

arXiv

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arXiv 2024年

作者： Peng, Jiaming Chen, Xinhai Liu, Jie Laboratory of Digitizing Software for Frontier Equipment Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Mesh generation is a crucial step in numerical simulations, significantly impacting simulation accuracy and efficiency. However, generating meshes remains time-consuming and requires expensive computational resources. In this paper, we propose a novel method, 3DMeshNet, for three-dimensional structured mesh generation. The method embeds the meshing-related differential equations into the loss function of neural networks, formulating the meshing task as an unsupervised optimization problem. It takes geometric points as input to learn the potential mapping between parametric and computational domains. After suitable offline training, 3DMeshNet can efficiently output a three-dimensional structured mesh with a user-defined number of quadrilateral/hexahedral cells through the feed-forward neural prediction. To enhance training stability and accelerate convergence, we integrate loss function reweighting through weight adjustments and gradient projection alongside applying finite difference methods to streamline derivative computations in the loss. Experiments on different cases show that 3DMeshNet is robust and fast. It outperforms neural network-based methods and yields superior meshes compared to traditional mesh partitioning methods. 3DMeshNet significantly reduces training times by up to 85% compared to other neural network-based approaches and lowers meshing overhead by 4 to 8 times relative to traditional meshing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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The research of a semantic architecture in meteorology grid computing

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APWeb 2006 International Workshops: XRA, IWSN, MEGA, and ICSE

作者： Kaijun, Ren Nong, Xiao Junqiang, Song Weimin, Zhang Peng, Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan 410073 China Science College National University of Defense Technology Changsha Hunan 410073 China Southwest Inst.of Electron. and Telecom.Tech. Shanghai 200434 China

ISBN: (纸本)3540311580

Meteorology is a complex, interdisciplinary area. Meteorology Grid Computing tries to offer a flexible, secure, coordinated resource sharing and problem-resolving environment by making good use of semantic grid ideas. In this paper, a semantic architecture in Meteorology Grid Computing is presented. With the architecture, we firstly discussed the Semantic Computing Service according to the semantic descriptions of grid computing resource. Then Semantic Data Access Service is illuminated by constructing automatic metadata mapping, which provides users with the convenience to quickly access data storage facilities in on-demand manner. Finally, Meteorology Grid Expert System is presented, and it will offer meteorology knowledge environments to facilitate the forecasters and scientists with analyzing the future weather situation. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Semantics

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