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1.
Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems. We define a network flow abnormal index as PDRA with the percentage of old IP addresses, the increment of the new IP addresses, the ratio of new IP addresses to the old IP addresses and average accessing rate of each new IP address. We design an IP address database using sequential storage model which has a constant time complexity. The autoregressive integrated moving average (ARIMA) trending prediction module will be started if and only if the number of continuous PDRA sequence value, which all exceed an PDRA abnormal threshold (PAT), reaches a certain preset threshold. And then calculate the probability that is the percentage of forecasting PDRA sequence value which exceed the PAT. Finally we identify the DDoS attack based on the abnormal probability of the forecasting PDRA sequence. Both theorem and experiment show that the method we proposed can effectively reduce the compute resources consumption, identify DDoS attack at its initial stage with higher detection rate and lower false alarm rate. 相似文献
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Jieren Cheng Junqi Li Xiangyan Tang Victor S. Sheng Chen Zhang Mengyang Li 《计算机、材料和连续体（英文）》2020,62(3):1423-1443
Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL)combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm. A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter. The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection, and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate. 相似文献
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刀具磨损是影响数控机床加工质量和加工效率的重要因素之一。针对现有铣刀磨损预测中信号单一和预测精度不足的问题,提出了一种基于注意力机制的堆叠LSTM (long short-term memory,长短期记忆网络) 的多传感器信息融合刀具磨损预测方法。对多传感器信号进行预处理,然后提取多域特征,利用核主成分分析法对其进行特征级信息融合,得到后续网络的输入。采用基于注意力机制的堆叠LSTM网络模型,使得网络能够自适应地学习数据的重要信息,在PHM2010的数据集上预测精度达到99.9%。通过与其他算法的对比试验和加入人工噪声的方法,验证了本文所提出的模型的高精度和鲁棒性。
相似文献4.
刀具磨损是影响数控机床加工质量和加工效率的重要因素之一。针对现有铣刀磨损预测中信号单一和预测精度不足的问题,提出了一种基于注意力机制的堆叠LSTM (long short-term memory,长短期记忆网络) 的多传感器信息融合刀具磨损预测方法。对多传感器信号进行预处理,然后提取多域特征,利用核主成分分析法对其进行特征级信息融合,得到后续网络的输入。采用基于注意力机制的堆叠LSTM网络模型,使得网络能够自适应地学习数据的重要信息,在PHM2010的数据集上预测精度达到99.9%。通过与其他算法的对比试验和加入人工噪声的方法,验证了本文所提出的模型的高精度和鲁棒性。 相似文献
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Xiangyan Tang Qidong Zheng Jieren Cheng Victor S. Sheng Rui Cao Meizhu Chen 《计算机、材料和连续体（英文）》2019,60(3):1263-1281
The existing network security situation assessment methods cannot effectively assess the Distributed denial-of-service (DDoS) attack situation. In order to solve these problems, we propose a DDoS attack situation assessment method via optimized cloud model based on influence function. Firstly, according to the state change characteristics of the IP addresses which are accessed by new and old user respectively, this paper defines a fusion feature value. Then, based on this value, we establish a V-Support Vector Machines (V-SVM) classification model to analyze network flow for identifying DDoS attacks. Secondly, according to the change of new and old IP addresses, we propose three evaluation indexes. Furthermore, we propose index weight calculation algorithm to measure the importance of different indexes. According to the fusion index, which is optimized by the weighted algorithm, we define the Risk Degree (RD) and calculate the RD value of each network node. Then we obtain the situation information of the whole network according to the RD values, which are from each network nodes with different weights. Finally, the whole situation information is classified via cloud model to quantitatively assess the DDoS attack situation. The experimental results show that our method can not only improve the detection rate and reduce the missing rate of DDoS attacks, but also access the DDoS attack situation effectively. This method is more accurate and flexible than the existing methods. 相似文献
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肖克;张建军;谭文武;王理;宋玲毓;林海军 《计量科学与技术》2024,68(10):38-44
为了降低传统CNN-LSTM模型进行水质预测时可能会出现的错误发生率,提出了一种基于Prophet模型与PCA的CNN-LSTM水质预测方法。在水质监测数据清洗过程中采用Prophet模型进行异常值处理,使用PCA方法对影响变量进行降维,消除变量关联性,把处理结果作为CNN-LSTM模型输入,对水质总氮指标进行预测。通过实验对基于Prophet模型与PCA的CNN-LSTM水质预测方法进行验证,实验结果表明：该方法相对于CNN-LSTM模型在MAE、RMSE和MSE三种评价指标上都有了较大的提升,其中MSE提升了13%,RMSE提升了6.7%,MAE提升了5.6%。 相似文献
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This study discusses the development and implementation of noncontact split detection method, for automotive stamping press lines. The system features a novel fusion routine that combines thermal and visible images in real-time, assisted with principle component analysis (PCA) subroutine. The thermal detector scans the temperature maps of the highly reflective steel sheets in the die cavity to locate abnormal temperature readings that might be indicative of high local wrinkling pressure, while the visible vision system offsets the blurring effect caused by heat diffusion across the surface and provide a spatial reference. The employed PCA uses a new singular value decomposition (SVD) that is more efficient than standard SVD computations, enabling the PCA to be applied in real-time acquisitions (～30 Hz). The PCA affects the images by reducing the nonvalue data content (reduce redundancy, noise) while highlighting important features. The fusion is done using a pixel-level algorithm using different variations, where each is assessed for performance. The proposed detection system has been tested on an automotive pressline to assess the formability of complex-shaped panels and have shown high detection success rate. Different splits with variant shape, size, and severity have been detected under actual operating conditions. 相似文献
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Jieren Cheng Yifu Liu Xiangyan Tang Victor S. Sheng Mengyang Li Junqi Li 《计算机、材料和连续体（英文）》2020,62(3):1317-1333
Distributed Denial-of-Service (DDoS) has caused great damage to the network in the big data environment. Existing methods are characterized by low computational efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary. Based on the network flow grayscale matrix feature (GMF), the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation, global features and local features of the network flow are extracted. A DDoS attack classifier based on multi-scale convolution neural network is constructed. Experiments show that compared with correlation methods, this method can improve the robustness of the classifier, reduce the false alarm rate and the missing alarm rate. 相似文献
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Abstract This paper is mainly concerned with the problem of distributing a data base (i.e., a set of segments) in a computer network system so as to facilitate parallel searching. In our distributed data base model, we assume that all segments are stored in nodes. Each time a query occurs, all nodes are searched concurrently. For convenience, we define the time required to access a segment from any node as a time unit. For a network with d nodes, the response time of a query is then identical to the maximum (n 1 , n 2, …, nd ), where ni , is the number of segments that satisfies the query and is stored in node i. Unfortunately, the solution for finding an optimal way to organize a distributed data base for parallel searching is still at large. In other words, given a data base, there is no efficient polynomial time algorithm for finding an optimal arrangement of segments onto nodes. In this article, we shall present a “heuristic algorithm” based upon a multivariant analysis method in statistics to distribute a data base in a network system. Some experimental results will show that our method is indeed feasible and effective. 相似文献
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一种基于奇异值分解的自适应降噪方法 总被引：4，自引：0，他引：4
根据信号处理基本理论和方法．针对奇异值分解方法中有关的Hankel矩阵有效秩难以确定的难题,提出了一种奇异值分解方法,即主分量分解方法．并通过试验数据进行了验证。仿真信号和海上实录信号的降噪实验研究表明．提出的方法比基本的LMS滤波和奇异值分解降噪效果更加优越,能有效提高信噪比并去除噪声。 相似文献
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Erik Vanhatalo Murat Kulahci 《Quality and Reliability Engineering International》2016,32(4):1483-1500
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目的研究光谱颜色复制中原稿图像的光谱信息,并对目标色的光谱反射率进行重构,探究影响重构光谱精度的因素。方法通过选取Munsell Color Matt(1269色块)和Color Checker Classic(24色块)2种色卡作为光谱反射率数据样本,建立不同的主成分分析线性重构模型,选取不同的基向量个数分别重构光谱,并对其精度进行评价,取Classic色卡模拟多光谱图像中重建光谱反射率的目标色,研究比较光谱重构模型和基向量数目对重构精度的影响。结果实验表明,降维模型1最终恢复的数据在RMSE,GFC和色差上均优于模型2,随着基向量数目的增加,2种降维模型差距在减小,当基向量数目达到13以后,2种模型基本没差异。结论文中提到光谱重建模型1和7个基向量是重构光谱图像的最佳方案。 相似文献
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Process monitoring and fault diagnosis using profile data remains an important and challenging problem in statistical process control (SPC). Although the analysis of profile data has been extensively studied in the SPC literature, the challenges associated with monitoring and diagnosis of multichannel (multiple) nonlinear profiles are yet to be addressed. Motivated by an application in multioperation forging processes, we propose a new modeling, monitoring, and diagnosis framework for phase-I analysis of multichannel profiles. The proposed framework is developed under the assumption that different profile channels have similar structure so that we can gain strength by borrowing information from all channels. The multidimensional functional principal component analysis is incorporated into change-point models to construct monitoring statistics. Simulation results show that the proposed approach has good performance in identifying change-points in various situations compared with some existing methods. The codes for implementing the proposed procedure are available in the supplementary material. 相似文献
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Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J. P. C. Rodrigues Bhawani Shankar Chowdhry Muhammad Aslam Uqaili Satya Prasad Majumder Danda B. Rawat Richard Etengu Rajkumar Buyya 《计算机、材料和连续体（英文）》2022,70(1):875-894
In the design and planning of next-generation Internet of Things (IoT), telecommunication, and satellite communication systems, controller placement is crucial in software-defined networking (SDN). The programmability of the SDN controller is sophisticated for the centralized control system of the entire network. Nevertheless, it creates a significant loophole for the manifestation of a distributed denial of service (DDoS) attack straightforwardly. Furthermore, recently a Distributed Reflected Denial of Service (DRDoS) attack, an unusual DDoS attack, has been detected. However, minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem. Moreover, recently the high frequencies of DDoS attacks have increased dramatically. In this paper, a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed. Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence. The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99% accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN. 相似文献
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ABSTRACTDuring the last decades, we evolved from measuring few process variables at sparse intervals to a situation in which a multitude of variables are measured at high speed. This evidently provides opportunities for extracting more information from processes and to pinpoint out-of-control situations, but transforming the large data streams into valuable information is still a challenging task. In this contribution we will focus on the analysis of time-dependent processes since this is the scenario most often encountered in practice, due to high sampling systems and the natural behavior of many real-life applications. The modeling and monitoring challenges that statistical process monitoring (SPM) techniques face in this situation will be described and possible routes will be provided. Simulation results as well as a real-life data set will be used throughout the article. 相似文献
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近红外光谱数据处理的独立分量分析方法研究 总被引：1，自引：1，他引：1
从数学的角度分析比较了主成分分析(PCA)与独立分量分析(ICA)的原理和特点,给出光谱矩阵在两种不同分析方法下的不同分解;同时结合线性回归和神经网络回归,提出"两步法"来确定不同成分含量测定的最优模型.进而采用PCA与ICA对实际测得的玉米近红外光谱进行了处理,比较分析了两种不同分解所得矩阵的化学含义,以及PCA与ICA两种不同分解对玉米光谱分析结果的影响.仿真结果表明,ICA从独立性角度对光谱数据矩阵进行分解,所得结果更接近实际光谱.最后,利用"两步法"对玉米三种主要成分水、淀粉、蛋白质分别建立了各自最优含量测定模型.结果表明,所建模型符合快速测定要求,具有一定的实用价值. 相似文献