Standardising vulnerability categories

Each vulnerability scanner (VS) represents, identifies and classifies vulnerabilities in its own way, thus making the different scanners difficult to study and compare. Despite numerous efforts by researchers and organisations to solve the disparity in vulnerability names used in the different VSs, vulnerability categories have still not been standardised. This paper highlights the importance of having a standard vulnerability category set. It also outlines an approach towards achieving this goal by generating a standard set of vulnerability categories. A data-clustering algorithm that employs artificial intelligence is used for this purpose. The significance of this research results from having an intelligent technique that aids in the generation of standardised vulnerability categories in a relatively fast way. In addition, the technique is generic in the sense that it allows one to accommodate any VS currently known on the market to create such vulnerability categories. Another benefit is that the approach followed in this paper allows one to also compare various VSs currently available on the market. A prototype is presented to verify the concept.     

VRSS: A new system for rating and scoring vulnerabilities

Vulnerabilities are extremely important for network security. IT management must identify and assess vulnerabilities across many disparate hardware and software platforms to prioritize these vulnerabilities and remediate those that pose the greatest risk. The focus of our research is the comparative analysis of existing vulnerability rating systems, so as to discover their respective advantages and propose a compatible rating framework to unify them. We do the statistic work on vulnerabilities of three famous vulnerability databases (IBM ISS X-Force, Vupen Security and National Vulnerability database) and analyze the distribution of vulnerabilities to expose the differences among different vulnerability rating systems. The statistical results show that the distributions of vulnerabilities are not much consistent with the normal distribution. Taking into account all kinds of existing vulnerability rating systems, we propose VRSS for qualitative rating and quantitative scoring vulnerabilities, which can combine respective advantages of all kinds of vulnerability rating systems. An experimental study of 33,654 vulnerabilities demonstrates that VRSS works well.     

基于主题爬虫的漏洞库维护系统

漏洞库是用来存储漏洞信息的数据库,是信息安全基础设施的重要组成部分。将主题爬虫技术引入漏洞数据库的维护工作,通过主题网络爬虫获取与"漏洞"相关的网页,从中提取漏洞信息来更新漏洞数据库,降低了人工维护的工作量,改善了现有漏洞库存在漏洞覆盖不全面、内容不丰富的问题。分析当前国内外主要漏洞库的结构特征,研究漏洞诸多属性间的关系,运用组群分类描述法构建漏洞库结构模型。在研究主题网络爬虫的基础上,提出一种面向漏洞主题的动态主题构建方案。介绍漏洞库维护系统的总体设计和实现方法。     

系统安全漏洞研究及数据库实现

计算机系统安全漏洞的研究以及漏洞库的建设，对于提高系统安全性，减少安全事件发生具有重要意义。该文在对系统安全漏洞分类研究的基础上，提出了一个系统安全漏洞数据库的结构模型，并以此构建了一个以国际CVE编号为标准的漏洞数据库，并给出了数据库中的一个漏洞实例。     

A method for identifying Web applications

Web applications are ubiquitous in today’s businesses. The security of these applications is of utmost importance since security breaches might negatively impact good reputation, and even result in bankruptcy. There are different methods of assessing security of Web applications, mainly based on some automated method of scanning. One type of scan method feeds random data to the application and monitors its behavior. The other type uses a database with predefined vulnerabilities that are checked one by one until either a vulnerability is found, or it can be claimed that the application does not have any known vulnerabilities. The important step in latter type of scan process is the identification of the application since in this case we are narrowing number of checks and, as a consequence, the scan process is faster. This paper describes a method for Web application identification based on a black box principle. Our method is based on the invariance of certain characteristics of Web applications. We experimentally tested and confirmed the usefulness of this approach.     

计算机弱点数据库综述与评价

计算机弱点数据库已成为弱点研究的重要组成部分，对收集、存储和组织弱点信息具有重要意义。本文介绍了计算机弱点的定义及分类，分析并评价了现有的弱点数据库，最后讨论了其存在的问题以及将采取的技术路线。     

An OVAL-based active vulnerability assessment system for enterprise computer networks

Many security problems are caused by vulnerabilities hidden in enterprise computer networks. It is very important for system administrators to have knowledge about the security vulnerabilities. However, current vulnerability assessment methods may encounter the issues of high false positive rates, long computational time, and requirement of developing attack codes. Moreover, they are only capable of locating individual vulnerabilities on a single host without considering correlated effect of these vulnerabilities on a host or a section of network with the vulnerabilities possibly distributed among different hosts. To address these issues, an active vulnerability assessment system NetScope with C/S architecture is developed for evaluating computer network security based on open vulnerability assessment language instead of simulating attacks. The vulnerabilities and known attacks with their prerequisites and consequences are modeled based on predicate logic theory and are correlated so as to automatically construct potential attack paths with strong operation power of relational database management system. The testing results from a series of experiments show that this system has the advantages of a low false positive rate, short running periods, and little impact on the performance of audited systems and good scalability. The security vulnerabilities, undetectable if assessed individually in a network, are discovered without the need to simulate attacks. It is shown that the NetScope system is well suited for vulnerability assessment of large-scale computer networks such as campus networks and enterprise networks. Moreover, it can also be easily integrated with other security tools based on relational databases.

基于知识图谱的网络安全漏洞智能检测系统设计

网络安全漏洞智能检测需要依赖大量的真实数据来进行分析,冗余数据与异常数据的存在会导致检测准确性下降;为保障网络系统稳定运行,提出基于知识图谱的网络安全漏洞智能检测系统设计研究;从结构、逻辑模型以及运行模式3个方面设计网络安全漏洞检测器,实现网络安全漏洞智能检测系统硬件设计;系统软件设计通过网络爬虫采集安全漏洞数据,去除冗余数据与异常数据,根据属性信息识别安全漏洞实体,获取安全漏洞属性信息关系,以此为基础,定义安全漏洞知识图谱表示形式,设计安全漏洞知识图谱结构,从而实现安全漏洞知识图谱的构建与可视化;以上述网络设计结果为依据构建网络安全漏洞智能检测整体架构,制定网络安全漏洞智能检测具体流程,从而获取最终网络安全漏洞智能检测结果;实验结果表明,在不同实验工况背景条件下,设计系统应用后的网络安全漏洞漏检率最小值为1.23%,网络安全漏洞检测F1值最大值为9.50,网络安全漏洞检测响应时间最小值为1 ms,证实了设计系统的安全漏洞检测性能更佳。     

通用Web漏洞库

本文研究了国内外Web漏洞库及建设的现状,设计并实现了一个专注于Web漏洞发布的Web漏洞数据库．文中兼顾了Web漏洞的固有特点及其与传统漏洞的属性差别,设计了Web漏洞库描述模型,丰富了Web漏洞的收集方法,定义了Web漏洞的漏洞评价属性标准,并在Web漏洞库中添加了Web漏洞重现模块．我们所设计的Web漏洞库确保了全面的Web漏洞信息收集和Web漏洞信息发布的标准化,可更好地对Web漏洞信息和数据进行分析研究,也为Web安全提供了有力的技术支撑．     

一种针对网络设备的已知漏洞定位方法

骨干级网络设备作为关键基础设施, 一直是网络攻防中的焦点, 与此同时, 其作为一个封闭、复杂的信息系统, 漏洞的公开研究资料相对较少、漏洞细节缺失较多。补丁对比是一种有效的漏洞分析手段, 而骨干级网络设备固件解包后通常具有单体式可执行文件, 这类文件具有函数数量多、文件规模大、调试符号信息缺失等特点, 直接进行补丁比对会产生大量待确认的误报差异, 同时启发式算法可能将两个不相关的函数错误匹配, 导致正确的安全修补缺失及漏报。传统的补丁比对方法无法有效地解决这类文件的补丁分析问题, 漏洞细节的分析遇到挑战。本文提出了一种针对单体式可执行文件中已知漏洞的定位方法MDiff, 通过漏洞公告描述中的子系统概念与目标二进制文件的内部模块结构对目标进行了拆分, 在基于局部性的二进制比对技术之上, 利用语义相似度衡量方法对比对结果进行筛选排序。具体来讲, MDiff首先利用入口函数及局部性原理识别存在漏洞的网络协议服务代码, 即粗粒度定位阶段。其次针对已识别出的、存在漏洞的网络协议服务代码模块中存在差异的函数进行动静态结合的语义信息分析, 包括基于扩展局部轨迹的安全修补识别, 基于代码度量的安全修补排序等步骤, 即细粒度定位阶段。基于该两阶段漏洞定位方法, 我们实现了一个原型系统, 对4个厂商设备中已经披露的15个漏洞进行实验。实验结果表明, 本文提出的漏洞定位方法可以提高网络设备的补丁分析效率, 支持研究人员发现已知漏洞细节。     

安全漏洞的统一描述研究

安全漏洞统一格式描述可以使不同的安全产品从同一描述进行漏洞信息的更新，减少了不同安全产品公司各自维护漏洞库的投入，达到了不同厂家安全产品漏洞信息的同步与数据描述的一致。本文叙述了利用XML进行安全漏洞统一格式描述的GVML实现及其在安全产品中的应用。     

基于区块链技术的网络安全漏洞检测系统设计

为解决传统网络安全漏洞扫描受限,导致网络应用环境的安全性承载能力较差的问题,设计基于区块链技术的网络安全漏洞检测系统;利用网络爬虫模块抓取任务管理模块所需的待检测信息参量,按照漏洞数据所属的具体类别,将其分别反馈至XSS检测模块、SQL检测模块与CSRF检测模块之中;在此基础上,定义区块信息的实际交易格式,联合各项智能化合约,实现对系统功能需求的定向化分析,并完成相关用例图的构建;通过上述软、硬件设备基础,完成基于区块链技术的网络安全漏洞检测系统设计;对比实验结果显示,与C/S型网络漏洞检测系统相比,基于区块链技术检测系统的安全性等级划分条件更加细致,扫描web漏洞覆盖范围也更为广泛,有助于网络应用环境安全性承载能力的稳定提升.     

基于搜索的物联网设备识别框架

越来越多的物联网设备接入到互联网中,但由于设计上的缺陷或者缺乏安全防护手段,这些暴露在公网上的物联网设备极容易受到黑客的攻击与利用。研究表明,具有相似产品属性的物联网设备很有可能存在相同漏洞,因此有效的识别网络空间中的物联网设备,对其产品属性,如设备品牌、型号等相关信息进行细粒度识别和标定,对把握网络空间实体设备的安全态势具有重要意义。本文提出一种基于搜索的物联网设备识别框架,利用物联网设备协议标语中富含的产品属性信息,通过自动化网络搜索技术构建物联网设备信息库,进而实现对未知新设备细粒度地自动分级识别和标定。通过公网实验,该框架能够很好识别视频监控和工控设备的产品属性,型号识别准确率均超过90%。     

Predicting Cyber Risks through National Vulnerability Database

ABSTRACT

Software vulnerabilities are the major cause of cyber security problems. The National Vulnerability Database (NVD) is a public data source that maintains standardized information about reported software vulnerabilities. Since its inception in 1997, NVD has published information about more than 43,000 software vulnerabilities affecting more than 17,000 software applications. This information is potentially valuable in understanding trends and patterns in software vulnerabilities so that one can better manage the security of computer systems that are pestered by the ubiquitous software security flaws. In particular, one would like to be able to predict the likelihood that a piece of software contains a yet-to-be-discovered vulnerability, which must be taken into account in security management due to the increasing trend in zero-day attacks. We conducted an empirical study on applying data-mining techniques on NVD data with the objective of predicting the time to next vulnerability for a given software application. We experimented with various features constructed using the information available in NVD and applied various machine learning algorithms to examine the predictive power of the data. Our results show that the data in NVD generally have poor prediction capability, with the exception of a few vendors and software applications. We suggest possible reasons for why the NVD data have not produced a reasonable prediction model for time to next vulnerability with our current approach, and suggest alternative ways in which the data in NVD can be used for the purpose of risk estimation.     

基于多粒度语义分析的二进制漏洞搜索方法

二进制文件相似度检测旨在通过比较来自不同平台、编译器、优化配置甚至是不同软件版本的2个二进制文件的相似程度来判断二者是否高度相似,其中二进制漏洞搜索为其在信息安全领域的应用之一。二进制漏洞的产生为现代软件应用带来了诸多问题,如操作系统易受攻击、隐私信息易被窃取等。二进制漏洞产生的主要原因是软件开发过程中进行了代码复用却没有进行严格的监管。据此,提出了一种基于多粒度语义特征分析的二进制漏洞搜索方法Taurus,该方法通过3种粒度的语义特征来搜索跨平台的潜在二进制漏洞。给定待检测二进制文件和漏洞数据库,需要对其与漏洞数据库中的每个二进制漏洞进行逐一搜索。首先,分别对2个二进制文件进行语义提取,以获取二者在基本块、函数和模块3个粒度下的语义特征,并执行相似度计算;然后,整合3种粒度下语义特征的相似度,以计算3种文件的整体相似度得分;最后,将待检测二进制文件与漏洞数据库中所有漏洞的相似度得分结果进行降序排序,便获得了该二进制文件的搜索结果报告。经过合理配置下的实验对比,结果表明, Taurus方法在准确性方面要优于基线方法。     

面向智能汽车的信息安全漏洞评分模型

随着汽车智能化、网联化的发展,汽车中集成了越来越多的电子器件,数量庞大的硬件、固件和软件中隐藏着各种设计缺陷和漏洞,这从根本上导致了智能汽车信息安全问题.大量汽车漏洞的披露,严重影响了汽车安全,制约了智能汽车的广泛应用.漏洞管理是降低漏洞危害、改善汽车安全的有效手段.在漏洞管理流程中,漏洞评估是决定漏洞处置优先级的重要...     

基于静态分析的安全漏洞检测技术研究

消除软件中的安全漏洞是建立安全信息系统的前提。静态分析方法可以自动地提取软件的行为信息,从而检测出软件中的安全漏洞。和其它程序分析方法相比,该方法具有自动化程度高和检测速度快的优点。在本文中,我们首先描述了静态分析的理论基础,然后说明了类型推断、数据流分析和约束分析等主要静态分析方法及其在安全漏洞检测中的应用,并比较这些方法的优缺点。最后给出了几种支持安全漏洞静态检测的编程语言。     

以太坊Solidity智能合约漏洞检测方法综述

以太坊Solidity智能合约基于区块链技术,作为一种旨在以信息化方式传播、验证或执行的计算机协议,为各类分布式应用服务提供了基础.虽然落地还不足6年,但因其安全漏洞事件频繁爆发,且造成了巨大的经济损失,使得其安全性检查方面的研究备受关注.首先基于以太坊相关技术对智能合约的一些特殊机制和运行原理进行介绍,并根据智能合约...     

An advanced approach for modeling and detecting software vulnerabilities

ContextPassive testing is a technique in which traces collected from the execution of a system under test are examined for evidence of flaws in the system.ObjectiveIn this paper we present a method for detecting the presence of security vulnerabilities by detecting evidence of their causes in execution traces. This is a new approach to security vulnerability detection.MethodOur method uses formal models of vulnerability causes, known as security goal models and vulnerability detection conditions (VDCs). The former are used to identify the causes of vulnerabilities and model their dependencies, and the latter to give a formal interpretation that is suitable for vulnerability detection using passive testing techniques. We have implemented modeling tools for security goal models and vulnerability detection conditions, as well as TestInv-Code, a tool that checks execution traces of compiled programs for evidence of VDCs.ResultsWe present the full definitions of security goal models and vulnerability detection conditions, as well as structured methods for creating both. We describe the design and implementation of TestInv-Code. Finally we show results obtained from running TestInv-Code to detect typical vulnerabilities in several open source projects. By testing versions with known vulnerabilities, we can quantify the effectiveness of the approach.ConclusionAlthough the current implementation has some limitations, passive testing for vulnerability detection works well, and using models as the basis for testing ensures that users of the testing tool can easily extend it to handle new vulnerabilities.