受免疫原理启发的Web攻击检测方法

随着Internet应用的不断深入,Web服务器成为了黑客的主要攻击目标。为克服传统误用入侵检测系统无法识别未知Web攻击和异常入侵检测系统误报率高等缺陷,受生物免疫系统启发,该文提出了一种基于免疫原理的Web攻击检测方法。给出了自体、非自体、抗原、抗体基因库、免疫细胞等的数学定义,描述了免疫学习算法。对比实验结果表明该方法较传统的基于神经网络和ID3算法的Web攻击检测技术能有效检测未知Web攻击,具有检测率和分类率高、误报率低和实时高效等特点,是检测Web攻击的一种有效新途径。     

需求分布规律变化情况下的报童订货策略

在需求分布规律变化情况下,报童在进行订货决策时会因为错误判断需求分布规律而导致期望库存成本增加。为了解决这一问题,本文集成传统历史需求信息和非传统需求信息以正确地认知需求分布规律,在此基础上决策订货量。假设需求服从均值不同、方差相同的两种类型的正态分布,每一种正态分布的概率已知。利用信号检测理论构建基于历史需求信息与需求分布概率的报童最优订货策略,并与只基于需求分布概率的直觉规则订货策略进行对比。结果表明：只要排除需求分布概率很大或很小两种极端情况,最优订货策略比直觉规则订货策略在控制期望库存成本方面的作用更明显,即利用历史需求信息可以有效修正报童对实际需求分布的检测结果,从而提高实际订货决策的准确性。研究结果对传统历史需求信息和非传统需求信息的集成以及需求信息交换等有一定的管理学启示和应用价值。     

加强大学生生命教育的几点思考

目前,我国高校学生出现了各种各样的问题,自杀现象非常严重,可见,此类问题的出现和生命教育的空白有直接的关系。本文从加强大学生生命教育的必要性与紧迫性,我国高校目前进行生命教育的现状,生命教育的主要内容和目前高校生命教育的对策几个方面来进行探讨。     

基于二阶段嵌套锡尔系数的人口分布的差异研究

文章首先提出了人口分布锡尔系数的概念及其一阶、二阶分解方法,接着以江苏省为研究区域,基于三大地带（苏南、苏中和苏北）、13个地级市、106个县（县级市、市辖区）三级结构的地域行政单元,分别运用一次、二次分解对全省的人口分布的区域差异进行细分测度．将江苏省人口分布的差异性分解为苏南、苏中、苏北三大地带闯的差异、三大地带内地级市之间的差异以及各地级市的内部差异三部分。其主要结论为：以地级市为计算单元的一阶段分解结果显示．苏南、苏中和苏北三个地带间人口分布的差异性是造成一阶段分解的全省人口分布差异的主要原因;以县级行政单元的二阶段分解结果表明．地级市内部的差异是江苏省人口分布的整体差异的主要构成部分;通过运用局部G统计量对各县级单元人口分布的热点探测发现,“热点”地区的人口分布差异性大．“冷点”地区的差异性小．     

Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.     

Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

FDR control of detected regions by multiscale matched filtering

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multiscale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal-to-noise ratio via matched filtering. This is achieved by considering a one-dimensional region detection problem as its equivalent zero-dimensional peak detection problem. The detection method does not require a priori knowledge of the shape of the nonzero regions. However, if the shape of the nonzero regions is known a priori, e.g., rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.     

On robustness of the Shiryaev–Roberts change-point detection procedure under parameter misspecification in the post-change distribution

The gist of the quickest change-point detection problem is to detect the presence of a change in the statistical behavior of a series of sequentially made observations, and do so in an optimal detection-speed-versus-“false-positive”-risk manner. When optimality is understood either in the generalized Bayesian sense or as defined in Shiryaev's multi-cyclic setup, the so-called Shiryaev–Roberts (SR) detection procedure is known to be the “best one can do”, provided, however, that the observations’ pre- and post-change distributions are both fully specified. We consider a more realistic setup, viz. one where the post-change distribution is assumed known only up to a parameter, so that the latter may be misspecified. The question of interest is the sensitivity (or robustness) of the otherwise “best” SR procedure with respect to a possible misspecification of the post-change distribution parameter. To answer this question, we provide a case study where, in a specific Gaussian scenario, we allow the SR procedure to be “out of tune” in the way of the post-change distribution parameter, and numerically assess the effect of the “mistuning” on Shiryaev's (multi-cyclic) Stationary Average Detection Delay delivered by the SR procedure. The comprehensive quantitative robustness characterization of the SR procedure obtained in the study can be used to develop the respective theory as well as to provide a rational for practical design of the SR procedure. The overall qualitative conclusion of the study is an expected one: the SR procedure is less (more) robust for less (more) contrast changes and for lower (higher) levels of the false alarm risk.     

Teachers’ Knowledge and Beliefs About Child Sexual Abuse

Child sexual abuse is one of the main types of abuse still to be addressed within the field of education, yet the education system itself can serve as a primary tool for its prevention. A better understanding of teachers’ knowledge and beliefs about child sexual abuse will allow us to establish key starting points from which to utilize the system for prevention. Four hundred and fifty teachers participated in this study, completing a questionnaire regarding their knowledge and beliefs about child sexual abuse. The study revealed that over half the teachers, 65.3% (n = 294), had never received any type of training in child sexual abuse education and that the majority were not familiar with methods of identifying child sexual abuse, 90.7% (n = 279). Various mistaken beliefs were identified among the participating teachers, such as pathological profiles of abusers, that the vast majority of child sexual abuse implies violent behavior, and that there cannot be abusers the same age as the victim. These results indicate that knowledge deficiencies do exist about child sexual abuse among teachers and highlight the need for training in this field.