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Adaptive and augmented active anomaly detection on dynamic network traffic streams

Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 446-460页

作者： Bin LI Yijie WANG Li CHENG National Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense TechnologyChangsha 410073China College of System Engineering National University of Defense TechnologyChangsha 410073China

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network ***,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on *** address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal *** one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of *** the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model *** experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

关键词： Active anomaly detection Network traffic streams Pseudo labels Prior knowledge of network attacks

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Ranking open source software based on crowd wisdom

Ranking open source software based on crowd wisdom

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Qiang Fan Huaimin Wang Gang Yin Tao Wang National Laboratory of Parallel and Distributed Computing National University of Defense Technology ChangSha Hunan China

ISBN: (纸本)9781479983544

Software reuse is critical in open source based software development, but it is very difficult to find a excellent reusable from large amount of similar candidate software in communities. Currently, lots of research works evaluate software by analyzing artifacts created by software developers, few of them reveals the power of feedbacks generated by software users, which we believe very valuable for software ranking. In this paper, we connect open source software from different communities with user feedbacks in StackOverflow, and explore the correlation between the popularity of posts and time. Finally we rank open source software through using information of connected posts in StackOverflow and compare our ranking result with several influential ranking results like DB-Engines and personal blogs. The comparison results show that our approach can amazingly give similar ranking results to that given by experienced professionals or commercial ranking systems.

关键词： Open source software Data models Joining processes Software reusability Internet

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parallel biomedical image processing with GPGPUs in cancer research

Parallel biomedical image processing with GPGPUs in cancer r...

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3rd IEEE International Symposium on Logistics and Industrial Informatics, LINDI 2011

作者： Reményi, Attila Szénási, Sándor Bándi, István Vámossy, Zoltán Valcz, Gábor Bogdanov, Pál Sergyán, Szabolcs Kozlovszky, Miklos Óbuda University John von Neumann Faculty of Informatics Budapest Hungary Semmelweiss University 2nd Department of Internal Medicine Budapest Hungary MTA SZTAKI/Laboratory of Parallel and Distributed Computing Budapest Hungary

ISBN: (纸本)9781457718410

The main aim of this work is to show, how GPGPUs can facilitate certain type of image processing methods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) stained colon tissue sample images. Since pathologists are working with large number of high resolution images - thus require significant storage space -, one feasible way to achieve reasonable processing time is the usage of GPGPUs. The CUDA software development kit was used to develop processing algorithms to NVIDIA type GPUs. Our work focuses on how to achieve better performance with coalesced global memory access when working with three-channel RGB tissue images, and how to use the on-die shared memory efficiently. © 2011 IEEE.

关键词： Image processing

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A Comparative Study of Star Graphs and Rotator Graphs

A Comparative Study of Star Graphs and Rotator Graphs

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International Conference on parallel Processing (ICPP)

作者： Subburajan Ponnuswamy Vipin Chaudhary Parallel and Distributed Computing Laboratory Department of ECE Wayne State University Detroit MI USA

Star graph is an extensively studied Cayley graph, considered to be an attractive alternative to the popular binary cube. The rotator graphs are a set of directed Cayley graphs introduced recently. In this paper we compare the structural and algorithmic aspects of star graphs with that of rotator graphs. In the process we present some new results for star graphs and rotator graphs. We present a formula for the number of nodes at any distance from the identity permutation in star graphs. The minimum bisection width of star and rotator graphs is obtained. Partitioning and fault tolerant parameters for both star and rotator graphs are analyzed. The node disjoint parallel paths and hence the upper bound on the fault diameter of rotator graphs are presented. We compare the minimal path routing in star and rotator graphs using simulation results.

关键词： Routing Fault tolerance Tin parallel processing distributed computing Laboratories Delay Computational modeling Upper bound Multiprocessing systems

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OSSEAN: Mining Crowd Wisdom in Open Source Communities

OSSEAN: Mining Crowd Wisdom in Open Source Communities

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2015 IEEE Symposium on Service-Oriented System Engineering

作者： Gang Yin Tao Wang Huaimin Wang Qiang Fan Yang Zhang Yue Yu Cheng Yang National Laboratory of Parallel and Distributed Computing National University of Defense Technology ChangSha Hunan China

Nowadays open source software represents a successful crowd-based software production model and is becoming an ecosystem combining huge amounts of software producers (such as software developers) and consumers (such as software users and customers). Lots of research work has been conducted on analyzing software artifacts created by producers, but few of them reveal the power of feedback from consumers which we believe is very important for the evaluation and evolution of open source software. This paper introduces OSSEAN, a platform for Open Source Software Evaluating, Analyzing and Networking. OSSEAN divides the open source communities into two groups: software production communities and software consumption communities. The former contain structured software artifacts such as projects, source code and issues, while the latter are full of textual documents with rich semantics of user feedback. We show the power of OSSEAN with some interesting demos by analyzing more than 200 thousands of open source projects and 10 million documents.

关键词： Communities Production Data mining Crawlers Open source software Ecosystems

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Secure execution of mobile programs

Secure execution of mobile programs

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DARPA Information Survivability Conference & Exposition

作者： R. Pandey B. Hashii M. Lal Parallel and Distributed Computing Laboratory Computer Science Department University of California Davis CA USA

There is increasing interest in computing models that support extensibility of systems through code migration. Although appealing both from the system design and extensibility points of view, extensible systems are vulnerable to an external program's aberrant execution behaviors. We examine the problems of resource access control and resource consumption. We propose solutions for these problems and analyze their effectiveness.

关键词： Operating systems Kernel Concurrent computing Computer science Internet Communication system security Government Laboratories Read only memory Java

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U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention

U-shaped Dual Attention Transformer: An Efficient Transforme...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Zhaoyuan Zhai Peng Qiao Rongchun Li Zhen Zhou National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Transformer-based methods have demonstrated remarkable performance on image super-resolution tasks. Due to high computational complexity, researchers have been working to achieve a balance between computation costs and performance. Restormer has achieved commendable balance by utilizing global channel attention. However, the performance is limited by insufficient local pixel reconstruction. In this paper, we propose a U-shaped Dual Attention Transformer (UDAT) with local-global receptive field, addressing the limitation of Restormer in local pixel reconstruction. We propose a dense window channel attention to enhance the local feature representation, more efficient in computational complexity. Experiments demonstrate that our UDAT achieve superior performance compared with Restormer on benchmark datasets, surpassing Restormer by 0.57 dB on the Urban100 dataset. On-par with SwinIR, our method reduces computational complexity by 3.2 times and improves inference speed by 2 times, achieving a better balance between computational costs and performance.

关键词： Visualization Superresolution Noise reduction Transform coding Transformers Image restoration Computational complexity Spatial resolution Image reconstruction Robots

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An incremental learning algorithm for supervised neural network with contour preserving classification

An incremental learning algorithm for supervised neural netw...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Piyabute Fuangkhon Thitipong Tanprasert Parallel and Distributed Computing Research Laboratory Faculty of Science and Technology Assumption University Bangkok Thailand

This paper presents an alternative algorithm for integrating the existing knowledge of a supervised learning neural network with the new training data. The algorithm allows the existing knowledge to age out in slow rate as a neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm also utilizes the contour preserving classification algorithm to increase the accuracy of classification. The experiment is performed on 2-dimension partition problem and the result convincingly confirms the effectiveness of the algorithm.

关键词： Neural networks Support vector machines Support vector machine classification Supervised learning Partitioning algorithms Neurons Classification algorithms Speech recognition Data mining distributed computing

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CPU resource control for mobile programs

CPU resource control for mobile programs

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International Symposium on Agent Systems and Applications. International Symposium on Mobile Agents

作者： M. Lal R. Pandey Parallel and Distributed Computing Laboratory Computer Science Department University of California Davis CA USA

There is considerable interest in developing runtime infrastructures for programs that can migrate from one host to another. Mobile programs are appealing because they support efficient utilization of network resources and extensibility of information servers. This paper presents a scheduling scheme for allocating resources to a mix of real-time and non real-time mobile programs. Within this framework, both mobile programs and hosts can specify constraints on how CPU should be allocated. On the basis of the constraints, the scheme constructs a scheduling graph on which it applies several scheduling algorithms. In case of conflicts between mobile program and host specified constraints, the schemes implement a policy that resolves the conflicts in favor of the host. The resulting scheduling scheme is adaptive, flexible, and enforces both program and host specified constraints.

关键词： Scheduling algorithm Resource management Java Web pages Government Security Concurrent computing Laboratories Computer science Runtime

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An Outpost Vector placement evaluation of an incremental learning algorithm for Support Vector Machine

An Outpost Vector placement evaluation of an incremental lea...

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International Joint Conference on Neural Networks (IJCNN)

作者： Piyabute Fuangkhon Thitipong Tanprasert Distributed and Parallel Computing Research Laboratory Faculty of Science and Technology Assumption University Bangkok Thailand

Outpost Vector model synthesizes new vectors at the boundary of two classes of data in order to increase the level of accuracy of classification. This paper presents a performance evaluation of four different placements of outpost vectors in an incremental learning algorithm for Support Vector Machine (SVM) on a non-complex problem. The algorithm generates outpost vectors from selected new samples, selected prior samples, both samples, or generates no outpost vector at all. After that, they are included in the final training set, as well as new samples and prior samples, based on the specified parameters. The experiments are conducted with a 2-dimension partition problem. The distribution of training and test samples is created in a limited location of a 2-dimension donut ring. There are two classes of data which are represented as 0 and 1. The context of the problem is assumed to shift 45 degrees in counterclockwise direction. Every consecutive partition is set to have different class of data. The experimental results show that the placement of outpost vectors generated from only selected new samples yields the highest level of accuracy of classification for both new data and old data. As a result, using samples from different part of the algorithm to generate outpost vectors affects the level of accuracy of classification.

关键词： Training Support vector machines Accuracy Shape Data models Context Vectors

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