联邦式脆弱性信息系统访问控制模型研究

深入分析脆弱性信息系统研究现状，指出现有脆弱性信息系统访问控制机制的不足之处。在分析联邦式系统特点的基础上，提出一种适用于联邦式脆弱性信息系统的访问控制模型，并介绍其实现技术要点。     

智能电网脆弱性综合评价模型构建及仿真

解决当前我国智能电网连锁故障导致的脆弱性问题,加强对电网的脆弱性评估是提高电网运行能力的重点。对此,结合当前智能电网脆弱性评估方法,提出一种基于TOPSIS的脆弱性综合评估方法。为提高综合评价的客观性,引入熵权法-AHP层次分析法对权重计算进行优化,然后通过TOPSIS模型完成对智能电网脆弱性的整体性评价。最后通过某实例,验证了上述方案的可行性,并得出我国电网在防御人为威胁等方面具有加强的优势,但是在信息安全方面存在一定的劣势。     

计算机网络脆弱性评价研究

计算机网络的应用越来越广泛,而计算机网络的信息安全问题也越来越突出。造成安全问题的根本原因在于计算机网络系统本身存在脆弱性。脆弱性评价是有效解决网络系统安全问题的必不可少的手段。首先介绍了脆弱性的基本概念,然后介绍了脆弱性评价的基本步骤及内容,包括评价的准备、脆弱性的识别、脆弱性的排序,其中对脆弱性识别作了重点的研究。     

IP多媒体子系统脆弱性评价技术研究

IP多媒体子系统（IMS）在带来网络融合以及业务提供能力增强的同时,也给基于IMS的下一代网络带来巨大的安全威胁,因而其脆弱性的研究工作受到广泛关注。借鉴了通用脆弱性评分系统（CVSS）的思想提取IMS脆弱性评估的指标,利用灰色理论建立了IMS脆弱性等级评价模型。构建了灰色理论的评价架构,列出了基于灰色理论的评分矩阵、三角权函数、灰色评价系数、权重矩阵、评价向量矩阵和评价结论值,为IMS脆弱性等级评定提供了科学依据。     

基于可靠性理论的分布式系统脆弱性模型

对现有的脆弱性分析方法进行分析和比较,提出基于可靠性理论的分布式系统脆弱性模型.针对影响分布式系统安全性的各项因素进行脆弱性建模,利用模型检验方法构造系统的脆弱性状态图,描述系统脆弱性的完整利用过程,引入可靠性理论对分布式系统的脆弱性进行分析和量化评估,从而为增强分布式系统的安全性提供理论依据.     

海南岛海岸侵蚀脆弱性评价

开展海南岛海岸侵蚀脆弱性评价对于沿海地区生态资源保护和灾害预防有着重要意义。采用生态系统服务和权衡的综合评估模型（Integrated Valuation of Ecosystem Services and Tradeoffs,InVEST）中的沿海脆弱性模型合成暴露度指数EI(Exposure Index)对海南岛海岸侵蚀脆弱性开展评价。针对典型研究区建立海岸特征—海岸动力—经济社会指标评价体系,优选海岸带特色评价因子包括海岸侵蚀速率、海岸类型、海岸生境等,进一步利用综合指数法量化脆弱性指数,最终获得海南岛沿海不同情境下的脆弱性,以及重点区域海岸侵蚀速率和侵蚀脆弱性等级。研究结果表明：(1)海南岛侵蚀脆弱性空间分布呈现东低西高,其中西南部市县脆弱性最高,东南部市县脆弱性最低,其余地区脆弱性适中。无生境情境下的沿海脆弱性远高于处于生境保护情境。(2)典型研究区海口东西海岸2016年至2020年砂质海岸受较多侵蚀,最多处超20 m/a。海口市龙华、美兰等主要城区海岸侵蚀脆弱性高,西海岸、东海岸分别次之,东寨港地区脆弱性最低。(3)研究发现在红树林等生境保护下的海岸可以有效得到保护,脆弱性...     

计算机系统脆弱性评估研究

在计算机安全领域,特别是网络安全领域,对计算机系统进行脆弱性评估十分重要,其最终目的就是要指导系统管理员在“提供服务”和“保证安全”这两者之间找到平衡。脆弱性评估方法的发展经历了从手动评估到自动评估的阶段,现在正在由局部评估向整体评估发展,由基于规则的评估方法向基于模型的评估方法发展,由单机评估向分布式评估发展。该文阐述了脆弱性评估所要解决的问题,介绍了目前在计算机系统脆弱性评估领域的主要方法以及今后的发展方向。     

一种Web应用软件安全脆弱性测试模型

研究使用错误注入的方法进行web应用软件脆弱性测试的技术,提出环境与状态错误模型(EAS模型),并提出基于EAS模型的web应用软件安全性测试方法.使用EAS模型对来自OWASP的漏洞数据库进行了分类,并与使用EAI模型分类的结果做了对比.使用EAS模型测试了WEB应用软件PEGames,发现了其中的6个漏洞.实验表明,EAS模型具有良好的漏洞覆盖能力和漏洞揭示能力.     

农产品跨境零售供应脆弱性评估模型仿真

针对当前评估模型存在农产品安全信息知晓率低、评估结果与实际结果拟合度低的问题,给为人民的生活和社会经济运行带来了严重的负面影响.提出基于群体灰色层次模糊法的农产品跨境零售供应脆弱性评估模型.简单介绍了农产品从农田到餐桌的每个环节,并分析影响农产品跨境零售供应脆弱性的影响因素,根据层次分析构建脆弱性评估指标体系,并对指标...     

电力监控系统脆弱性评估模型研究

针对目前电力系统脆弱性缺乏有效的评估机制和方法,根据电力监控系统的特点,对安全需求多样性和业务流程协同性造成的脆弱性评估难点进行分析,结合电力监控系统的信息结构,定义满足要求的脆弱性分析模型和量化模型,通过构建典型的评估算例,验证脆弱性评估模型的有效性.     

Estimation of urban air pollution

The problem of real-time estimation of air pollutant concentrations in an urban atmosphere based on concentration measurements made intermittently at a set of monitoring stations is considered. A square-root distributed parameter filter for a general class of dynamic urban air pollution models is developed. The filter is tested by application to a hypothetical urban area, and the effect of the number of monitoring stations on the estimated concentrations is studied.     

Improved parameterisation for the numerical modelling of air pollution within an urban street canyon

Numerical modelling for application to wind flow and dispersion in urban environments has noticeably progressed in recent years, to currently represent a widely used tool for simulating mechanical processes governing air pollution in complex geometries. In particular, Computational Fluid Dynamic (CFD) techniques based on RANS (Reynolds-Averaged Navier–Stokes equations) models, are extensively used to produce detailed simulations of the wind flow and turbulence in the urban canopy. However, several studies have indicated that RANS models, and in particular the widely used standard k–? turbulence model, are sensitive to the particular form of inlet profiles for turbulence and velocity. In the present study, simulations of the wind flow and dispersion within an idealised street canyon were carried out using the standard k–? turbulence model provided by the commercial software FLUENT. The aim of this study was to improve the standard k–? model performance by modifying the model parameters according to the chosen form of inlet profiles for velocity and turbulence. Capability of the model to reproduce real wind flow fields, turbulence and concentration patterns was evaluated by comparing the model results against recently published wind tunnel data. Results for turbulent kinetic energy and concentration showed that the redefinition of the default dispersive parameters can significantly enhance the model performance. The newly proposed parameterisations of the standard k–? turbulence model can be readily implemented within commercial CFD software packages, offering a reliable modelling tool for application to urban air pollution and other environmental studies.     

Deriving high-resolution urban air pollution maps using mobile sensor nodes

Up-to-date information on urban air pollution is of great importance for environmental protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 50 million measurements. We collected the measurements throughout more than two years using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100 m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with sub-weekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error–a standard metric to evaluate the accuracy of air quality models–of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.     

A finite difference model for air pollution simulation

A 3-D model for atmospheric pollutant transport is proposed considering a set of coupled convection–diffusion–reaction equations. The convective phenomenon is mainly produced by a wind field obtained from a 3-D mass consistent model. In particular, the modelling of oxidation and hydrolysis of sulphur and nitrogen oxides released to the surface layer is carried out by using a linear module of chemical reactions. The dry deposition process, represented by the so-called deposition velocity, is introduced as a boundary condition. Moreover, the wet deposition is included in the source term of the governing equations using the washout coefficient. Before obtaining a numerical solution, the problem is transformed using a terrain conformal coordinate system. This allows to work with a simpler domain in order to build a mesh that provides finite difference schemes with high spatial accuracy. The convection–diffusion–reaction equations are solved with a high order accurate time-stepping discretization scheme which is constructed following the technique of Lax and Wendroff. Finally, the model is tested with a numerical experiment in La Palma Island (Canary Islands).     

An enhanced model for evacuation vulnerability assessment in urban areas

Rapid urbanization has led to a massive influx of people into cities. When many people congregate in urban areas, crowd crushing emergencies are likely to occur. If vulnerable areas with potential evacuation problems are detected in advance, crowd crushing emergencies may be minimized or even avoided. Thus, an evacuation vulnerability assessment from a precautionary perspective is fundamental. However, the current evacuation vulnerability assessment models are limited in spreading time estimation and evacuation capacity evaluation. To mitigate these limitations, in this study, we propose an enhanced model to quantitatively assess the evacuation vulnerability in urban areas. Our model enhances the current models in two ways. First, we employ a hexagon gridding scheme to construct a network to meet the prerequisite of evacuation spreading at the equal time intervals. Second, we quantify the grid connectivity on the network by considering the grid capacity to avoid underestimation of the evacuation vulnerability. Using a mobile phone location dataset of Shanghai, we systematically investigate the evacuation vulnerability of urban areas in a fine-grained spatio-temporal scale. Areas that may encounter evacuation problems to various degrees can be identified in advance. This information can support emergency management agencies in monitoring dense crowds and ensure early warnings of potential crowd crushing emergencies.     

Application of ADIFOR for air pollution model sensitivity studies

Typical computational methods of sensitivity analysis are discussed. Automatic differentiation addresses the need for computing derivatives of large codes accurately, regardless of the complexity of the model. Automatic differentiation in FORTRAN (ADIFOR) is a source transformation technique that accepts FORTRAN coded program for the computation of a function and generates portable FORTRAN code for the computation of the derivatives of that function. ADIFOR is introduced and applied to a comprehensive atmospheric chemistry/transport/radiative-transfer model to study the sensitivity of photochemical ozone production with respect to aerosol. The modeling results indicate that aerosol interaction with ozone may be as important as NO_x and non-methane hydrocarbon (NMHC) emissions in determining ozone production. The presence of scattering and/or absorbing aerosols in the atmosphere can cause significant differences in calculated ozone levels. Normalized sensitivity coefficients show that ozone and other photochemical oxidants are most sensitive to the aerosol single scattering albedo, which determines the scattering efficiency of the aerosol. ADIFOR is demonstrated to be an effective tool for sensitivity analysis in air pollution modeling.     

Learning probabilistic models for model checking: an evolutionary approach and an empirical study

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically “learn” models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? Moreover, how well does learning scale to real-world applications? If the answer is negative, what are the potential methods to improve the efficiency of learning? In this work, we first investigate existing algorithms for learning probabilistic models for model checking and propose an evolution-based approach for better controlling the degree of generalization. Then, we present existing approaches to learn abstract models to improve the efficiency of learning for scalability reasons. Lastly, we conduct an empirical study in order to answer the above questions. Our findings include that the effectiveness of learning may sometimes be limited and it is worth investigating how abstraction should be done properly in order to learn abstract models.     

Equity warrants pricing model under Fractional Brownian motion and an empirical study

In this paper, we construct equity warrants pricing model under Fractional Brownian motion, deduce the European options pricing formula with a simple method, then propose the warrants pricing formula, and extend it to cover equity warrants on a stock providing dividends. Finally, taking Changdian warrant in Chinese stock market as an example, we illustrate that the results based on the new warrants pricing formula is more accuracy than the classical results based on traditional pricing model.