A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

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.     

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

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.     

An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment

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.     

反馈信息数据融合算法在实时数据处理中的应用

本文研究分析了融合无反馈信息雷测设备局部状态估计,引入了反馈数据信息的光测设备目标局部状态估计获得目标融合航迹的算法,并通过在光测和雷测设备实时数据融合处理中的试验验证,证明了该算法可有效改善目标跟踪精度,减小系统的动态误差.     

基于数据融合和小波分析的结构损伤诊断

基于数据融合和小波分析理论，提出一种新的结构损伤诊断方法。采用改进的一致性算法融合多传感器的测量数据，克服了一致性算法中两传感器在测量精度不同时置信距离不同的缺点，对支持矩阵进行模糊化处理，避免了人为定义阈值而产生的主观误差。利用小波分析的降噪和多尺度分辨能力对多传感器的数据进行分析处理，从而对结构损伤作出诊断识别。通过数值算例，验证了该方法可以充分利用所有传感器的有效信息，能够在部分传感器性能降低（如受到噪声影响），甚至是完全失效的情况下，对结构损伤作出正确诊断。     

一种基于粗糙集的模糊信息融合方法及应用

将粗糙集理论和模糊逻辑技术结合起来，提出了一种基于粗糙集数据处理的模糊信息融合方法。运用粗糙集的基本理论和简约计算方法，从大量原始数据中发现精简的、概略化的规则，结合模糊逻辑推理建立一致粗糙模糊模型，并提出了对模型进行扩充与完备化的概念。脉动真空灭菌温度控制过程的仿真试验研究结果表明了所提方法的有效性和可行性。     

Research on the Information Fusion Method of Oil Diagnosis and Vibration Diagnosis Based on the Rough Sets Theory

The improved method has been presented for knowledge reduction in rough sets （R-S） theory, when R-S is used to model the information expression of oil and vibration diagnosis. Therefore, the typical fault simulation tests of rolling bearings have been made, and the application method of R-S has been also analysed in this paper. The diagnosis model of holding rack fault in rolling bearing was presented based on the improved reduction method. It is suited to information fusion to combine information when oil analysis and vibration analysis are combined for fault diagnosis.     

雷达光电经纬仪外测数据的融合处理方法

为了提高飞行器的事后定位精度,提出了一种基于多台雷达光电经纬仪外测数据融合的处理方法.给出了雷达光电经纬仪外测数据的数学模型,将样条约束方法应用于角度、距离和速度测量数据的融合处理,建立了飞行器位置参数和设备系统误差的联合求解模型,通过充分利用雷达测量数据来增加模型的冗余度.仿真结果表明,与单纯的角度数据融合相比,角度、距离和速度测量数据进行融合可以显著提高飞行器位置参数和设备系统误差的估计精度.     

基于局部特征融合的人脸识别

提出了基于局部特征融合的人脸识别算法.首先把人脸图像分割为多个子图像,利用传统主成分分析的方法,对不同位置的子图像集分别建立不同的子空间并且抽取相应的局部特征.针对各局部特征,分别求出待识别图像对训练样本的隶属度.最后,基于模糊综合的原理对各局部特征进行数据融合,给出最终识别结果.实验结果表明,该算法能很好地融合人脸的局部信息,有效提高识别率.     

基于信息融合方法的切削颤振状态识别技术

讨论了切削颤振状态识别的信息融合方法．分别给出了基于基本概率分配函数和基于证据区间值的颤振状态识别方法。试验中，在同一个测量区内使用了功率传感器和加速度传感器，利用Dempster—Shafer证据论方法对两种传感器信息进行了分析融合。试验与理论分析表明：经过信息融合得到的基本概率分配函数可以作为一种颤振状态识别参数。如果同时考虑证据区间P1(A)-Bel(A)值进行识别会减小识别的不确定性．提高颤振状态识别的精度。     

Cross-Lingual Non-Ferrous Metals Related News Recognition Method Based on CNN with A Limited Bi-Lingual Dictionary

To acquire non-ferrous metals related news from different countries’ internet, we proposed a cross-lingual non-ferrous metals related news recognition method based on CNN with a limited bilingual dictionary. Firstly, considering the lack of related language resources of non-ferrous metals, we use a limited bilingual dictionary and CCA to learn cross-lingual word vector and to represent news in different languages uniformly. Then, to improve the effect of recognition, we use a variant of the CNN to learn recognition features and construct the recognition model. The experimental results show that our proposed method acquires better results.     

A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.     

基于信息融合模型的心脏病急救信息共享系统

心脏病急救过程中产生的信息呈现出多维度、动态性和不确定性的特点.为了有效地应用这些信息,提出了一个4层的行为体系结构.在这一结构的基础上,提出了一种两阶段心脏病信息融合推理机制.基于这一推理机制,建立了心脏病急救信息共享系统.     

斯米尔诺夫检验法在数据级信息融合数据关联中的应用

在信息融合系统中，为了确保进入融合中心的信息是满足融合需要的有效信息，在融合之前要对来自多个信息源的数据进行数据关联。作为信息融合前的信号预处理过程，利用数理统计中的斯米尔诺夫检验法对多个同类传感器测得的加速度响应信号进行数据关联，去掉故障传感器或受到严重干扰的通道数据，确保进入融合中心的数据能够有效反映被测对象的状态。通过某二级齿轮箱实验台实测数据的验证，证明该方法能够有效地检验出同类传感器信号是进行故障诊断的有效信号。     

基于数据仓库与数据挖掘技术的包装信息处理方法研究

针对包装信息的特点,阐述了包装信息数据仓库的建立方法,把各种包装信息内部数据和外部数据进行有效集成,并同时阐述了在包装信息数据仓库下进行包装信息数据挖掘的方法,实现了将包装信息中的数据挖掘技术集成到复杂的包装信息技术应用环境中,为各层次的研究决策、分析人员服务。     

一种基于数据融合的图像插值算法

针对传统的线性插值算法存在的边缘模糊问题,本文提出一种新算法.首先采用距离平方反比的插值方法在插值点邻域内计算水平、垂直和对角三个方向共6个插值,然后以插值距离和方向梯度构造权重,进行数据融合获得最终插值.该算法既考虑了插值距离因素,又考虑了插值方向梯度信息,有效地保护插值图像的边缘和纹理信息.实验结果表明,该算法的插值图像比传统的双线性插值法均方误差降低而平均梯度增加,是一种提高插值图像分辨率的有效方法.     

基于多传感器数据融合的热处理炉温度测量方法

温度是制约热处理性能的主要指标,对热处理炉温度的准确、可靠测量是目前亟待解决的技术问题。本提出了一种基于多传感器参数估计数据融合的热处理炉温度测度方法,给出数据融合算法。实际应用结果验证了算法的准确性。     

基于多尺度Kalman数据融合滤波

本文通过分析基于小波变换的动态系统模型,提出一种基于小波多尺度的Kalman数据滤波方法,本文利用小波的多尺度特点,把初始估计序列多尺度分解,并在不同尺度层上进行Kalman滤波估计,再利用小波重构来融合各层的估计信息,把标准Kalman滤波只在单一尺度和时间轴上对状态估计值和误差协方差进行数据更新,改进为基于小波变换的尺度轴和时间轴上的双向数据更新,该算法将小波多尺度分解去噪和Kalman滤波相结合,对实际中含较强噪声的动态系统的状态估计效果较好.算法也可用于多分辨率多传感器数据融合.     

基于多尺度信息融合的层次聚类算法

在体绘制领域和图像分割中,数据集通常具有流形结构,各部分边界连接紧密且伴随局部噪声,给传统聚类算法的应用带来了较大的困难．本文根据非参数密度估计方法提出了一种基于多尺度信息融合的层次聚类算法．新算法通过整合密度差异和边界信息构造了一种多尺度结构信息融合的相似性度量,通过水平集的图连接策略推导出一种层次化的类结构剖析过程以获取稳定的聚类结果．新算法不受数据集形状、密度类型的限制,无需对数据集进行假设,可自动识别数据集常见的聚类结构特征．同时聚类结果较为稳定,算法对噪声具有较强的鲁棒性．从人工数据集和真实数据集以及应用试验的测试结果可以看出新算法的优越性能．     

一种基于小波变换的目标融合跟踪方法

本文在分析小波变换的基础上，将小波分析应用到目标图像的融合跟踪技术上，利用小波的多尺度和多分辨特性，不仅能够获得不同分辨力下的图像序列，进行目标图像融合；还能有效地从信号中提取突变信号。对函数或信号进行多尺度的细化分析。图像边缘用小波变换进行处理和提取并对图像形心进行计算。能够得到较好的轮廓提取效果和形心定位精度，进而说明了小波变换可能成为目标跟踪中较好的数学方法。