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基于数据挖掘的网络入侵检测系统研究 总被引：1，自引：0，他引：1
针对传统入侵检测系统建模与更新需要大量人工参与,提出一种基于数据挖掘的无指导自适应入侵检测系统.系统通过有效结合聚类、关联规则数据挖掘方法,自动进行检测规则的提取.经实验表明,提出的方法具有较好的检测率、误报率. 相似文献
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何立仁;彭博;池明旻 《计算机科学》2025,52(2):202-211
无监督异常检测因只需要正常样本进行训练而被广泛应用于工业质检等领域。直接将现有的单类别异常检测方法应用到多类别异常检测中会导致性能显著下降,其中基于知识蒸馏的异常检测方法将预训练的教师模型关于正常样本的特征知识蒸馏到学生模型中,然而它们在多类别异常检测中存在无法保证学生模型只学习到正常样本知识的问题。文中提出一种基于反向知识蒸馏框架的无监督多类别异常检测方法(Prototype based Reverse Distillation,PRD ),其通过Multi-class Normal Prototype模块和Sparse Prototype Recall训练策略来学习教师模型关于多类别正常样本特征的 Prototype,并以此来过滤学生模型的输入特征,从而确保学生模型只学习到教师模型关于正常样本的特征知识。PRD在多种工业异常检测数据集上性能均超越了现有的SOTA方法,定性、定量和消融实验验证了PRD整体框架和内部模块的有效性。 相似文献
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This paper is about extracting knowledge from large sets of videos, with a particular reference to the video-surveillance application domain. We consider an unsupervised framework and address the specific problem of modeling common behaviors from long-term collection of instantaneous observations. Specifically, such data describe dynamic events and may be represented as time series in an appropriate space of features. Starting off from a set of data meaningful of the common events in a given scenario, the pipeline we propose includes a data abstraction level, that allows us to process different data in a homogeneous way, and a behavior modeling level, based on spectral clustering. At the end of the pipeline we obtain a model of the behaviors which are more frequent in the observed scene, represented by a prototypical behavior, which we call a cluster candidate. We report a detailed experimental evaluation referring to both benchmark datasets and on a complex set of data collected in-house. The experiments show that our method compares very favorably with other approaches from the recent literature. In particular the results we obtain prove that our method is able to capture meaningful information and discard noisy one from very heterogeneous datasets with different levels of prior information available. 相似文献
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针对虚拟机进行异常检测是提高云计算系统可靠性的重要手段之一。然而,云环境中虚拟机的性能指标数据具有维度高、信息冗余等特点,会降低检测效率和准确度。同时,传统异常检测方法难以定量刻画系统的异常状态,而局部异常因子(Local Outlier Factor, LOF)算法虽可量化其异常程度,但它以相同权重计算不同维度变量对系统状态的影响,导致算法对异常的区分能力减弱。针对以上问题,提出一种高效的异常检测策略。该策略以最大相关最小冗余算法和主成分分析法对性能指标进行筛选降维,提高了异常检测的效率;为LOF算法中不同维度的变量赋予不同权重,强化了不同指标对异常的区分度。实验表明,该策略相对于传统异常检测方法,效率和检测率都有显著提高。 相似文献
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This paper reports the application of deep learning for implementing the anomaly detection of defects on concrete structures, so as to facilitate the visual inspection of civil infrastructure. A convolutional autoencoder was trained as a reconstruction-based model, with the defect-free images, to rapidly and reliably detect defects from the large volume of image datasets. This training process was in the unsupervised mode, with no label needed, thereby requiring no prior knowledge and saving an enormous amount of time for label preparation. The built anomaly detector favors minimizing the reconstruction errors of defect-free images, which renders high reconstruction errors of defects, in turn, detecting the location of defects. The assessment shows that the proposed anomaly detection technique is robust and adaptable to defects on wide ranges of scales. Comparison was also made with the segmentation results produced by other automatic classical methods, revealing that the results made by the anomaly map outperform other segmentation methods, in terms of precision, recall, F1 measure and F2 measure, without severe under- and over-segmentation. Further, instead of merely being a binary map, each pixel of the anomaly map is represented by the anomaly score, which acts as a risk indicator for alerting inspectors, wherever defects on concrete structures are detected. 相似文献
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云存储技术因其能为用户提供安全、海量、随时随地的数据存储功能而得以快速发展.提出利用网络上现存的自然分布于世界各地的大量免费存储服务,如网盘、ftp、email以及其他形式等存储空间,将这些服务集中起来,为用户提供价廉、可靠的存储系统.介绍一种低开销、快速度、高可用、可扩展的云存储拓扑系统,以期在该拓扑架构下买现以文件... 相似文献
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Scheduling is essentially a decision-making process that enables resource sharing among a number of activities by determining their execution order on the set of available resources. The emergence of distributed systems brought new challenges on scheduling in computer systems, including clusters, grids, and more recently clouds. On the other hand, the plethora of research makes it hard for both newcomers researchers to understand the relationship among different scheduling problems and strategies proposed in the literature, which hampers the identification of new and relevant research avenues. In this paper we introduce a classification of the scheduling problem in distributed systems by presenting a taxonomy that incorporates recent developments, especially those in cloud computing. We review the scheduling literature to corroborate the taxonomy and analyze the interest in different branches of the proposed taxonomy. Finally, we identify relevant future directions in scheduling for distributed systems. 相似文献
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信息技术发展日新月异,基于云计算的应用正在兴起。世界大学城应时推出,职教新干线的崭新应用成为教育界的新期待和新希望。本文从问题的提出、设计思路、研究平台几个方面入手提出了基于世界大学城的云计算辅助教学下的协作学习的一种设计方案。 相似文献
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Detecting anomalies in time series in real time can be challenging, in particular when anomalies can manifest themselves at different time scales and need to be detected with minimal latency. The need for lightweight real-time algorithms has risen in the context of Cloud computing, where thousands of devices are monitored and deviations from normal behaviour must be detected to prevent incidents. However, this need has yet to be addressed in a way that actually scales to the size of today’s network infrastructures.Typically, time series generated by human activity often exhibit daily and weekly patterns creating long-term dependencies that are difficult to process. In such cases, the euclidean distance between subsequences of the time series, or euclidean anomaly score, can be a very effective tool to achieve good detection within constrained latency; however, this computation has a quadratic complexity and a computational footprint too high for any realistic application.In this paper, we propose SCHEDA (Sampled Causal Heuristics for Euclidean Distance Approximation), a collection of three heuristics designed to approximate the euclidean anomaly score with a low computational footprint in time series with long-term dependencies. Our design goals are a low computational cost, the possibility of real-time operation and the absence of tuning parameters. We benchmark SCHEDA against ARIMA and the euclidean distance and show that in typical monitoring scenarios, it outperforms both at only a fraction of the computational cost. 相似文献
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蔡国瑜 《计算机光盘软件与应用》2011,(17)
随着计算机技术的不断向前发展,云计算和云存储作为一种崭新的模式,愈发受到个人和企业的关注。本文在对云计算进行简要介绍的基础上,对云计算环境下的数据存储即云存储体进行了分析和研究,展示了云存储的巨大优势,并就制约云存储进一步发展的若干问题进行了讨论。 相似文献
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Jamie Shiers 《Computer Physics Communications》2009,180(4):559-563
By the time of CCP 2008, the largest scientific machine in the world - the Large Hadron Collider - had been cooled down as scheduled to its operational temperature of below 2 degrees Kelvin and injection tests were starting. Collisions of proton beams at 5+5 TeV were expected within one to two months of the initial tests, with data taking at design energy (7+7 TeV) foreseen for 2009.In order to process the data from this world machine, we have put our “Higgs in one basket” - that of Grid computing [The Worldwide LHC Computing Grid (WLCG), in: Proceedings of the Conference on Computational Physics 2006 (CCP 2006), vol. 177, 2007, pp. 219-223]. After many years of preparation, 2008 saw a final “Common Computing Readiness Challenge” (CCRC'08) - aimed at demonstrating full readiness for 2008 data taking, processing and analysis. By definition, this relied on a world-wide production Grid infrastructure.But change - as always - is on the horizon. The current funding model for Grids - which in Europe has been through 3 generations of EGEE projects, together with related projects in other parts of the world, including South America - is evolving towards a long-term, sustainable e-infrastructure, like the European Grid Initiative (EGI) [The European Grid Initiative Design Study, website at http://web.eu-egi.eu/]. At the same time, potentially new paradigms, such as that of “Cloud Computing” are emerging.This paper summarizes the results of CCRC'08 and discusses the potential impact of future Grid funding on both regional and international application communities. It contrasts Grid and Cloud computing models from both technical and sociological points of view. Finally, it discusses the requirements from production application communities, in terms of stability and continuity in the medium to long term. 相似文献
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Kris Bubendorfer Kyle Chard Koshy John Ashfag M. Thaufeeg 《Future Generation Computer Systems》2013,29(8):2143-2156
Social networks offer great potential for fostering collaboration between individuals and amongst groups. This potential collaborative environment is not only applicable for recreation, but can also provide considerable value to diverse research communities. For this reason scientists are increasingly utilizing social networking concepts in projects to form groups, share information, publicize their work and communicate with their peers. This article describes two different approaches to supporting eScience, by providing scientific computing and collaboration within what we term the Social Cloud. In our first approach the social network is used as a collaborative overlay, in combination with the ad hoc creation of infrastructure composed of virtual machine clusters built from resources contributed, by the users, to the Social Cloud. Our second approach is based around the principle of volunteer computing, where the Social Cloud provides researchers with a platform to exploit social networks by reaching out to non technical users who would otherwise be unlikely to donate computational time for scientific and other research. In this article we specifically explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through the use of incentives in existing social networks. 相似文献
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Automated live video stream analytics has been extensively researched in recent times. Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame. We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score. The second stage uses the least square method for optimal anomaly score generation. The third stage adopts award-based reinforcement learning to update the model. The proposed Hybrid Ensemble RR Model was tested on standard pedestrian datasets UCSDPed1, USDPed2. The data set has 70 videos in UCSD Ped1 and 28 videos in UCSD Ped2 with a total of 18560 frames. Since a real-time stream has strict memory constraints and storage issues, a simple computing machine does not suffice in performing analytics with stream data. Hence the proposed research is designed to work on a GPU (Graphics Processing Unit), TPU (Tensor Processing Unit) supported framework. As shown in the experimental results section, recorded observations on frame-level EER (Equal Error Rate) and AUC (Area Under Curve) showed a 9% reduction in EER in UCSD Ped1, a 13% reduction in ERR in UCSD Ped2 and a 4% improvement in accuracy in both datasets. 相似文献
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In many domains, the previous decade was characterized by increasing data volumes and growing complexity of data analyses, creating new demands for batch processing on distributed systems. Effective operation of these systems is challenging when facing uncertainties about the performance of jobs and tasks under varying resource configurations, e. g., for scheduling and resource allocation. We survey predictive performance modeling (PPM) approaches to estimate performance metrics such as execution duration, required memory or wait times of future jobs and tasks based on past performance observations. We focus on non-intrusive methods, i. e., methods that can be applied to any workload without modification, since the workload is usually a black box from the perspective of the systems managing the computational infrastructure. We classify and compare sources of performance variation, predicted performance metrics, limitations and challenges, required training data, use cases, and the underlying prediction techniques. We conclude by identifying several open problems and pressing research needs in the field. 相似文献
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边缘计算将云计算扩展到网络边缘,在解决了云计算时延高、移动性差和位置感知弱等缺陷的同时也带来了诸多安全问题;针对边缘计算网络开放性、异构型和节点资源受限等特点,研究设计具有6层结构的通用边缘计算入侵检测系统,并在此模型架构上提出了一个边缘计算入侵检测方案,基于该方案提出了一种适用于边缘计算部署的改进极限学习机的入侵检测算法TSS-ELM,TSS-ELM增加了云服务器训练样本筛选环节来优化机器学习中的外权,从而对边缘节点数据实现高效的入侵检测;仿真实验结果和分析表明,该算法在准确性、时间依赖性、鲁棒性和误报率方面与其他现有算法相比具有更优异的性能. 相似文献