The relationship between centrality and land use patterns: Empirical evidence from five Chinese metropolises

The relationship between location and land use patterns is one of the classic theoretical issues in urban studies. Classic models based on the monocentricity hypothesis have limitations in the interpretation of modern urban structure. China has experienced institutional transformation in recent decades, and the interaction of national government policy, market forces and the natural environment has influenced urban planning in Chinese metropolises, resulting in urban structures with special characteristics. This paper examines the distribution of location and land use intensity, and tested the Alonso model by the relationship between them in five Chinese metropolises using Point of Interest data, space syntax methodology, the grid weighted statistical method and the Geographically Weighted Regression (GWR) model. Universal patterns about the scaling relation between intensity of land use types and the centrality of location are revealed. The elasticity of land use types to location, from high to low sensitivity, is commercial, residential then industrial land in most of the five metropolises studied. The sensitivity sequence of land use studied suggests that the hypothetical model based on the classical Alonso model can explain the spatial structure of modern metropolises in China to some extent, especially for the commercial land. But the order of sensitivity of residential land and industrial land to location does not conform to the model. The spatial heterogeneity in land use intensity and centrality were explored and the factors embedded were discussed. It can be found that the relation between centrality and land use intensity conforms to power law. In most of the metropolises studied, when the scaling relation between land use intensity and centrality is super linear, the sequence of the frequency value from high to low are commercial, residential and industrial land; when the scaling relation is sublinear, the sequence of the frequency value is industrial, residential and commercial land.     

石油石化系统言息安全态势评估指标体系研究

文章阐述了石油石化系统信息安全态势评估指标体系的研究意义,提出了指标体系的研究思路,分析了指标体系的基本构成,说明了指标体系的总体框架,并对指标体系的数据获取和应用前景作了介绍。对于丰富和完善国家信息安全态势评估指标体系,是一个有意义的探索。     

基于迁移学习的小样本DGA恶意域名检测方法

域名生成算法（DGA）存在变化多、部分类别样本难获取的特点,使得采用传统机器学习的恶意域名检测模型准确性不高。提出一种基于迁移学习和多核CNN的小样本DGA恶意域名检测模型。该模型将目标域名映射到向量空间中,使用样本充足的DGA种类进行预训练,并迁移预训练得到的参数到小样本检测模型。采用多核CNN小样本分类模型根据发音习惯进行域名特征提取并分类。通过实验对比发现,无知识迁移的小样本分类模型只有11类域名准确率超过92%,经过迁移学习的多核CNN模型20类准确率超过92%,11类准确率超过97%,检测效果接近数据充足时的分类效果。     

工控网络协议Fuzzing测试技术研究综述

随着信息化和工业化的深度融合,控制系统在工业生产过程中得到了越来越广泛的应用,很多研究者开始针对工控系统,特别是具有控制功能的工控网络协议的安全性展开研究,漏洞分析则是其中较为活跃的领域之一.由于工控网络协议具有专用性和面向控制的特点,通常在封闭环境下运行,无法直接应用传统Fuzzing测试技术进行网络协议的漏洞挖掘.本文阐述了工控网络协议的特点以及Fuzzing测试的困难,讨论并比较了现有各种Fuzzing测试技术应用于工控网络协议的优缺点,提出工控网络协议的专用Fuzzing测试工具的设计准则,最后展望了工控网络协议Fuzzing测试技术的未来研究方向.     

A taint based approach for automatic reverse engineering of gray-box file formats

File format vulnerabilities have been highlighted in recent years, and the performance of fuzzing tests relies heavily on the knowledge of target formats. In this paper, we present systematic algorithms and methods to automatically reverse engineer input file formats. The methodology employs dynamic taint analysis to reveal implicit relational information between input file and binary procedures, which is used for the measurement of correlations among data bytes, format segmentation and data type inference. We have implemented a prototype, and its general tests on 10 well-published binary formats yielded an average of over 85 % successful identification rate, while more detailed structural information was unveiled beyond coarse granular format analysis. Besides, a practical pseudo-fuzzing evaluation method is discussed in accordance with real-world demands on security analysis, and the evaluation results demonstrated the practical effectiveness of our system.     

代码相似性检测技术:研究综述

程序代码的相似性检测是使用一定的检测手段度量程序代码间的相似程度,其对于提升高等教育中计算机课程教学效果和保护软件知识产权都有着重要的意义.介绍了代码相似性检测技术的研究意义和发展历程,阐述了本领域研究过程中的概念模型,深入分析了已有的几类代码相似性检测技术,总结了这几类技术各自的特点,同时探讨了一些相关研究,最后归纳了目前研究中的问题并展望了本领域研究的发展趋势.     

Windows安全之SEH安全机制分析

SHE（结构化异常处理）,是Windows操作系统所提供的对错误或异常的一种处理机制。但是,由于SEH设计存在缺陷,使得在存在缓冲区溢出时,攻击者可以利用SEHC-J异常处理链结构实施成功的攻击。所以,微软在不同版本的Windows操作系统中分别都SEH实现上做了相应的改动,分别出现了SafeSEH和SEHOP两个改进机制。但是,在改进之后的SEH机制中又分别出现了不同程度的缺陷。本文结合微软的GS、ASLR等技术对不同版本的SEH机制的实现以及其存在的安全缺陷进行深入研究,并针对安全缺陷提出相应的修改意见。     

ARCE风险处理计划测量模型的设计与实现

提出用于定量测量风险处理计划有效性的ARCE模型,从理论上证明该模型的正确性.以组织信息安全资产的风险值和已实施控制措施为输入,通过中间变量矩阵得到风险处理计划有效性矩阵的模型量化指标体系.模型实现过程包括风险评估、风险处理、定量测量、安全事件管理和报表5个部分,并给出实施流程.应用结果表明,该模型能准确测量风险处理计...     

A feature dependent Naive Bayes approach and its application to the software defect prediction problem

Naive Bayes is one of the most widely used algorithms in classification problems because of its simplicity, effectiveness, and robustness. It is suitable for many learning scenarios, such as image classification, fraud detection, web mining, and text classification. Naive Bayes is a probabilistic approach based on assumptions that features are independent of each other and that their weights are equally important. However, in practice, features may be interrelated. In that case, such assumptions may cause a dramatic decrease in performance. In this study, by following preprocessing steps, a Feature Dependent Naive Bayes (FDNB) classification method is proposed. Features are included for calculation as pairs to create dependence between one another. This method was applied to the software defect prediction problem and experiments were carried out using widely recognized NASA PROMISE data sets. The obtained results show that this new method is more successful than the standard Naive Bayes approach and that it has a competitive performance with other feature-weighting techniques. A further aim of this study is to demonstrate that to be reliable, a learning model must be constructed by using only training data, as otherwise misleading results arise from the use of the entire data set.     

一种Android应用程序恶意行为的静态检测方法

目前Android应用程序的安全问题得到越来越多的关注. 提出一种检测Android应用程序中恶意行为的静态分析方法, 该方法采用静态数据流分析技术, 并实现了常量分析算法, 通过跟踪应用程序对常量值的使用来检测恶意订购、资费消耗等多种类型的恶意行为. 实验结果表明, 该方法可以有效检测出Android应用程序的恶意行为, 具有较高的实用性.