基于模糊支持向量机的网络入侵检测技术

针对支持向量机算法中存在对噪声数据和异常数据敏感的问题,提出了模糊支持向量机算法,并应用于入侵检测.该算法是在传统支持向量机分类器的构造方法中引入隶属度函数,根据不同输入所得到的分类结果,产生相应的惩罚值.将这个方法应用到入侵检测系统中,能较好地将正常数据和异常数据区分开.实验结果表明,采用模糊支持向量机的入侵检测技术,其误报率低于基于支持向量机的入侵检测,同时其检测率也相对较高.     

基于支持向量机的异常检测

提出一种使用支持向量机(SVM)进行计算机系统实时异常检测的方法，内容涉及到一种对支持向量机方法的改进算法、对数据预处理的方法及SVM核函数的选取．试验结果表明采用这一算法进行入侵检测具有准确率高、计算简单、占用的存储空间小等优点．     

基于支持向量机的CAN-FD网络异常入侵检测

针对汽车潜在的网络攻击行为,对于车载灵活数据速率控制器局域网络（CAN-FD）提出了一种基于支持向量机的异常入侵检测算法。在通用入侵检测框架（CIDF）下,该方法使用报文标识符（ID）、时间周期和数据场数据作为入侵检测特征,利用支持向量机算法的二分类特性和小样本特征,实现了对CAN-FD网络环境下入侵报文数据的识别。仿真实验数据表明,所提出的方法具有较高的入侵检测正确率,且可用于周期性报文和非周期性报文。     

基于支持向量机与k近邻相结合的网络入侵检测研究

针对基于支持向量机算法的网络入侵检测方法,对交错严重的大规模复杂样本集,分类面附近的样本被正确分类可信度低,基于k近邻算法的检测方法测试结果不稳定等问题,提出一种将k近邻与支持向量机相结合的网络入侵检测方法。在分类时,计算待识别的网络连接记录样本与最优分类超平面的距离,如果距离大于预设阀值,采用支持向量机算法对连接记录进行分类,否则采用k近邻法对连接记录进行分类,同时为减少检测方法受样本分布不均衡的影响,采用带权重因子的欧式距离来度量样本间的相似度。最后在KDD99数据集上进行仿真实验,结果表明,相对于单一的支持向量机或k近邻算法的网络入侵检测方法,k近邻与支持向量机相结合的入侵检测方法可以进一步提高网络入侵检测的正确率,是一种较好的网络入侵检测方法。     

基于特征选择和支持向量机的异常检测

为在提高检测率的同时保持较低的虚警率,提出一种不同于单一算法的基于特征选择和支持向量机的异常检测技术.首先用一个数据子集进行特征选择,通过构造函数来计算特征间的相似度,并在不失样本信息的前提下,完成会话样本的特征选择;然后对用于训练和测试的数据执行特征选择,剔除不必要的数据特征;最后利用支持向量机来判断入侵.仿真实验表明:与基于标准的支持向量机、基于tf×idf的支持向量机、基于tf×idf的神经网络及基于数据挖掘等的异常检测相比较,该方法具有更高检测率,同时其虚警率也更低.     

基于支持向量机和遗传算法融合的入侵检测

为了研究网络异常入侵检测问题,将支持向量机（SVM）和遗传（GA）算法融合并应用于入侵检测领域,区分正常和异常的用户行为,实现对网络系统的入侵检测.传统SVM算法易产生训练参数选择不当,难以获得较高的检测效率和分类精度等问题.针对此问题,提出了一种优化的基于SVM-GA融合的入侵检测方法,首先对网络入侵数据进行归一化处理简化输入,然后通过遗传算法对SVM训练参数进行同步优化,最后采用SVM算法对网络数据进行检测,分类识别得到网络入侵结果.仿真实验结果表明,该融合算法训练时间短、检测精度高、误报率和漏报率低,是一种有效可行的入侵检测方法.     

基于空间降维和多核支持向量机的网络入侵检测

提出空间降维和多核支持向量机算法进行网络入侵检测;从网络中抓取数据包,通过局部线性嵌入数据降维获得属性降维后的数据样本;通过差异化设置参与局部线性嵌入运算的邻居数,验证适合样本集的邻居数,将多项式核函数、高斯核函数和Sigmoid核函数进行两两组合,分别验证多核支持向量机的网络入侵检测性能。结果表明,在合理设置局部线性嵌入邻居数和核函数组合方式的条件下,基于空间降维和多核支持向量机的网络入侵检出率较高,检测时间较短。     

基于MIKPSO-SVM方法的工业控制系统入侵检测

针对Kalman粒子群算法在优化基于支持向量机的工业控制系统入侵检测模型时易陷入局部极小的问题,该文提出了一种改进的多新息Kalman粒子群算法。所提算法不仅考虑当前粒子信息的观测值,同时充分利用之前时刻的有用信息对粒子的状态进行估计,为粒子位置的更新提供足够的冲量,使得算法跳出局部极小,从而提高了算法的优化精度。将所提出的改进算法用于支持向量机工控入侵检测模型参数寻优,并使用工控入侵检测标准数据集进行仿真研究。仿真结果表明:与Kalman粒子群、粒子群以及遗传算法相比,该文所提出的算法——优化的支持向量机入侵检测模型在检测率、漏报率和误报率等指标上都有明显提升。     

基于SVM的分布式入侵检测系统

支持向量机（Support Vector Machine．简称SVM）具有泛化性高、全局最优、对样本的充分性要求不高等优点,在集中式的入侵检测问题中得到较好应用．文章将SVM算法推广到分布式入侵检测环境中,提出基于SVM的分布式学习算法。并在KDD Cup 99数据集上与集中式方式进行了对比实验．结果表明,该算法不仅能降低网络中的通信负载．而且取得了与集中式方式相当的检测性能．     

一种基于动态类中心模型选择的模糊支持向量机

模糊支持向量机的核心思想是赋予样本模糊隶属度,给每个样本以不同的权重,从而克服标准支持向量机对噪声和异常点敏感的问题.现有的模糊支持向量机算法通常以样本与类中心距离为基础,给每个样本赋予一个固定的隶属度,没有根据样本分布对隶属度做进一步修正.提出了一种新的动态方式赋予样本隶属度,利用萤火虫算法不断地更新样本中心的位置和隶属度函数,同时利用粒子群算法优化模糊支持向量机参数.在UCI数据集上的实验结果表明,该算法可以有效减少噪声和野点对超平面的影响,分类性能要优于几类常用的模糊支持向量机算法.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

Exciton Seebeck in Molecular Systems

Various applications relevant to the exciton dynamics,such as the organic solar cell,the large-area organic light-emitting diodes and the thermoelectricity,are operating under temperature gradient.The potential abnormal behavior of the exicton dynamics driven by the temperature difference may affect the efficiency and performance of the corresponding devices.In the above situations,the exciton dynamics under temperature difference is mixed with     

Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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An on-line scaling method for improving scalability of a database cluster

The explosive growth of the Internet and database applications has driven database to be more scalable and available, and able to support on-line scaling without interrupting service. To support more client's queries without downtime and degrading the response time, more nodes have to be scaled up while the database is running. This paper presents the overview of scalable and available database that satisfies the above characteristics. And we propose a novel on-line scaling method. Our method improves the existing on-line scaling method for fast response time and higher throughputs. Our proposed method reduces unnecessary network use, i.e. , we decrease the number of data copy by reusing the backup data. Also, our on-line scaling operation can be processed parallel by selecting adequate nodes as new node. Our performance study shows that our method results in significant reduction in data copy time.     

An improved R-tree based on childnode''s probability

R-Tree is a good structure for spatial searching. But in this indexing structure,either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R-tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R-tree (PBAR-tree).     

Integrating GIS Web services based on mediating architecture

The geographic information service is enabled by the advancements in general Web service technology and the focused efforts of the OGC in defining XML-based Web GIS service. Based on these models, this paper addresses the issue of services chaining,the process of combining or pipelining results from several interoperable GIS Web Services to create a customized solution. This paper presents a mediated chaining architecture in which a specific service takes responsibility for performing the process that describes a service chain. We designed the Spatial Information Process Language (SIPL) for dynamic modeling and describing the service chain, also a prototype of the Spatial Information Process Execution Engine (SIPEE) is implemented for executing processes written in SIPL. Discussion of measures to improve the functionality and performance of such system will be included.     

Tracking data management using topology of moving object database system for moving object constrained trajectory in LBS application

Advances in wireless technologies and positioning technologies and spread of wireless devices, an interest in LBS (Location Based Service) is arising. To provide location based service, tracking data should have been stored in moving object database management system (called MODBMS) with proper policies and managed efficiently. So the methods which acquire the location information at regular time intervals then, store and manage have been studied. In this paper, we suggest tracking data management techniques using topology that is corresponding to the moving path of moving object. In our techniques, we update the MODBMS when moving object arrived at a street intersection or a curved road which is represented as the node in topology and predict the location at past and future with attribute of topology and linear function. In this technique, location data that are corresponding to the node in topology are stored, thus reduce the number of update and amount of data. Also in case predicting the location,because topology are used as well as existing location information, accuracy for prediction is increased than applying linear function or spline function.