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Jisuanji Xuebao/Chinese Journal of Computers 2012年第6期35卷 1192-1208页

作者： Wang, Tian-Zuo Wang, Huai-Min Liu, Bo Shi, Pei-Chang Institute of Network and Information Security School of Computer Science National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China

As a complex, flexible and effective platform for network attacking, the botnet spreads widely in the Internet. Botnets can provide the botmasters with the ability to launch large-scale malicious activities such as spamming and DDoS (Distributed Denial of Service) attacks. Botnets are continuously bringing more and more severe threats, so that the study on botnets has already become one of the focuses in the field of network security. However, in recent years, some new developments of botnets are challenging the existing understanding on botnets. In this paper, according to the new conditions of botnets and the researches in existence, a definition of botnet is proposed based on the works of other researchers, taxonomies of botnets are introduced respectively from the views of network structure, dependency and delivery pattern of C&C (Command and Control) information. Then the techniques on detecting, measuring and restraining botnets are analyzed systematically. In the end, we give the evolution trends of botnets and the future research trends in this area.

关键词： Botnet

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Two-dimensional euler PCA for face recognition 21

Two-dimensional euler PCA for face recognition

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Tan, Huibin Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Principal component analysis (PCA) projects data on the directions with maximal variances. Since PCA is quite effective in dimension reduction, it has been widely used in computer vision. However, conventional PCA suffers from following deficiencies: 1) it spends much computational costs to handle high-dimensional data, and 2) it cannot reveal the nonlinear relationship among different features of data. To overcome these deficiencies, this paper proposes an efficient two-dimensional Euler PCA (2D-ePCA) algorithm. Particularly, 2D-ePCA learns projection matrix on the 2D pixel matrix of each image without reshaping it into 1D long vector, and uncovers nonlinear relationships among features by mapping data onto complex representation. Since such 2D complex representation induces much smaller kernel matrix and principal subspaces, 2D-ePCA costs much less computational overheads than Euler PCA on large-scale dataset. Experimental results on popular face datasets show that 2D-ePCA outperforms the representative algorithms in terms of accuracy, computational overhead, and robustness. © Springer International Publishing Switzerland 2015.

关键词： Principal component analysis

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Non-negative low-rank and group-sparse matrix factorization 21

Non-negative low-rank and group-sparse matrix factorization

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Wu, Shuyi Zhang, Xiang Guan, Naiyang Tao, Dacheng Huang, Xuhui Luo, Zhigang Science and Technology on Parallel Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan410073 China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan410073 China Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia

ISBN: (纸本)9783319144412

Non-negative matrix factorization (NMF) has been a popular data analysis tool and has been widely applied in computer vision. However, conventional NMF methods cannot adaptively learn grouping structure froma *** paper proposes a non-negative low-rank and group-sparse matrix factorization (NLRGS) method to overcome this deficiency. Particularly, NLRGS captures the relationships among examples by constraining rank of the coefficients meanwhile identifies the grouping structure via group sparsity regularization. By both constraints, NLRGS boosts NMF in both classification and clustering. However, NLRGS is difficult to be optimized because it needs to deal with the low-rank constraint. To relax such hard constraint, we approximate the low-rank constraint with the nuclear norm and then develop an optimization algorithm for NLRGS in the frame of augmented Lagrangian method(ALM). Experimental results of both face recognition and clustering on four popular face datasets demonstrate the effectiveness of NLRGS in quantities. © Springer International Publishing Switzerland 2015.

关键词： Non-negative matrix factorization

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

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14th IEEE International Conference on Software Engineering and Service science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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JointCloud Cross-chain Verification Model of Decentralized Identifiers

JointCloud Cross-chain Verification Model of Decentralized I...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Tao Zhong Peichang Shi Junsheng Chang Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Institute of Computer Technology College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665443326

When multiple entities communicate or collaborate in JointCloud, identities are the very prior basis to build trust with each other. Decentralized identifier (DID) can provide a trusted identity with blockchain technology and a complete method of identity verification based on verifiable credentials, which solves problems of conventional centralized identity. However, current DIDs can only conduct verification within a single blockchain, which limits the interoperability of DIDs on different blockchains. Network isolation hinders the verification of DIDs on different blockchains and thus there is a need to break the barrier between blockchains. In this paper, we propose a model to conduct cross-chain verification of DIDs. We build a system of credit evaluation to describe the credibility of DIDs in a unified way and deploy smart contracts to implement cross-chain verification of DIDs. Experimental results verifies the feasibility of the model, which realizes cross-chain verification of DIDs in the networks of blockchain.

关键词： Couplings Computational modeling Scalability Conferences Smart contracts Prototypes Data structures

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DaBA: Data-free Backdoor Attack against Federated Learning via Malicious Server

DaBA: Data-free Backdoor Attack against Federated Learning v...

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Image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Kuiqi Chen Liming Fang Mengxin Wang Changchun Yin College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China

Current research shows that the privacy of FL is threatened by an honest-but-curious server. However, existing research focus on privacy attacks against the malicious server while overlooking that it could also compromise the shared model's integrity by introducing poisoning attacks. In this work, we propose a novel data-free backdoor attack (DaBA) against FL via malicious server to bridge the gap. Specifically, we utilize global model inversion to obtain a dummy dataset on the server side, then add backdoor triggers to a portion of the inputs in the dummy dataset and replace their labels with the target label, and finally retrain part of the global model on the poisoned dummy dataset. Our experimental results show that DaBA can achieve a high attack success rate on poisoned samples and high prediction accuracy on clean samples, which means the effectiveness and stealthiness of DaBA, respectively. For example, in the experiment of the MNIST dataset, DaBA can achieve a 99.6% attack success rate and 96.3% accuracy rate. We also discuss possible defense strategies against our attack. Our research reveals a significant security risk of FL.

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Stigmergic Collaboration: A Sign-Based Perspective of Stigmergy

Stigmergic Collaboration: A Sign-Based Perspective of Stigme...

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2010 International Conference on Intelligent Computation technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

作者： Chuanjun Ren Hongbing Huang Shiyao Jin National Laboratory for Parallel & Distributed Processing College of Computer Science National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781424472796

Insects build architecturally complex nests and search for remote food by collaboration work despite their limited sensors, minimal individual intelligence and the lack of a central control system. Insets' collaborations emerge as a response of the individual insects to Stigmergy. A sign-based model of Stigmergy to discuss collaboration is proposed in this paper where we picked up "sign" as a key notion to understand it. Therefore, sign is the link of all the components in a Stigmergic complex adaptive system. Based on this understanding, we propose a definition that reveals the nature of signs and exploit the significations and relationships carried by the notion of sign. Then, a sign-based model of Stigmergy is consequently reached, which captures the essentials of Stigmergy. A basic architecture of Stigmergy as well as its constituents are presented and discussed. At last, some applications of the model are discussed.

关键词： Stigmergy Sign Emergence Collaboration CAS

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The Efficient In-band Management for Interconnect Network in Tianhe-2 System

The Efficient In-band Management for Interconnect Network in...

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Euromicro Conference on parallel, Distributed and Network-Based processing

作者： Jijun Cao Liquan Xiao Zhengbin Pang Kefei Wang Jiaqing Xu College of Computer National University of Defense Technology China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China

Interconnect network plays an important role in high performance computing systems. And its manageability directly affects the RAS (i.e., Reliability, Availability, and Serviceability) of the whole system. The Tianhe-2 system located in NSCC-gz (i.e., National Supercomputing Center of China in Guangzhou) uses proprietary interconnect network, which includes 5,856 high-radix network router chips (i.e., NRC) and 18,304 network interface chips (i.e., NIC). For such a very large-scale interconnect network, it is a great challenge to manage (such as configure, monitor, and debug) the numerous network chips and its network ports in an efficient way. By implementing the in-band management with very few hardware resources, the interconnect network in Tianhe-2 system achieves a highly efficient network management. In this paper, we introduce the design and implementation of the in-band management for interconnect network in Tianhe-2 system, especially emphasizing on several key features, including the set of achieved management functionalities, the architecture of network management, the format of management packets, the data flow and processing of management packets, etc. In this paper, we also evaluate the performance of in-band management by mainly comparing with out-band management scheme. The preliminary results demonstrate the efficiency of the in-band management for interconnect network in Tianhe-2 system.

关键词： Routing Ports (Computers) Network topology Optical switches Hardware Topology Registers

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MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data

MIX: A Joint Learning Framework for Detecting Both Clustered...

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IEEE International Conference on Data Mining (ICDM)

作者： Hongzuo Xu Yijie Wang Yongjun Wang Zhiyue Wu National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Another kind of approaches separately evaluates outlierness in numerical and categorical features. However, they fail to adequately consider the behaviours of data objects in different feature spaces, often leading to suboptimal results. As for outlier form, both clustered outliers and scattered outliers are contained in many real-world data, but a number of outlier detectors are inherently restricted by their outlier definitions to simultaneously detect both of them. To address these issues, an unsupervised outlier detection method MIX is proposed. MIX constructs a joint learning framework to establish a cooperation mechanism to make separate outlier scoring constantly communicate and sufficiently grasp the behaviours of data objects in another feature space. Specifically, MIX iteratively performs outlier scoring in numerical and categorical space. Each outlier scoring phase can be iteratively and cooperatively enhanced by the prior knowledge given by another feature space. To target both clustered and scattered outliers, the outlier scoring phases capture the essential characteristic of outliers, i.e., evaluating outlierness via the deviation from the normal model. We show that MIX significantly outperforms eight state-of-the-art outlier detectors on twelve real-world datasets and obtains good scalability.

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GPS: A General Framework for parallel Queries over Data Streams in Cloud

GPS: A General Framework for Parallel Queries over Data Stre...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xiaoyong Li Yijie Wang Yu Zhao Yuan Wang Xiaoling Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

parallel query processing over data streams in cloud computing environments has attracted considerable attention recently in various fields, due to the huge potential value of analyzing massive data or big data in a large number of streaming applications. Nevertheless, existing studies on queries primarily focus on the algorithms for the specific query types with the lack of the general framework for processing various queries. Moreover, existing parallel frameworks in cloud such as MapReduce and its variations are not suitable for many complex queries over complex data streams. In this paper, we extensively discuss the problem of designing the general framework for parallel queries over data streams in cloud. Particularly, we propose and implement a framework called GPS, which can be well adapted to various queries over complex data streams like the uncertain data streams. Furthermore, we further propose a hierarchical and general parallel model for queries over data streams based on the proposed framework, which is more flexible than the MapReduce model. The skyline queries over uncertain data streams based on our proposed framework with real deployment are conducted as an example to verify the performances of our proposals.

关键词： Peer-to-peer computing parallel processing Data models Query processing Global Positioning System Object oriented modeling Distributed databases

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