基于指令相对吞吐率的虚拟机检测方法

对于常见的虚拟机检测方法仅适用于特定配置的虚拟机的不足,设计并实现一种基于特权指令与非特权指令的相对吞吐率变化的检测虚拟机的方法,检测过程采用查询-验证的方式。该方法可以成功地分辨出物理机环境与虚拟机环境的不同,并且事先只需了解较少的待检测目标的配置信息,对不同硬件配置的依赖性较小。     

基于双线性对的无证书签名与群签名方案

传统数字签名方案的证书存储和管理开销较大,基于身份的数字签名方案无法解决其固有的密钥托管问题,而无证书签名方案无需使用公钥证书,且没有密钥托管问题。为此,提出一个基于双线性映射的无证书签名方案,并在随机预言机模型下证明其安全性。在此基础上设计一个无证书群签名方案,其安全性建立在计算Diffie-Hellman问题的困难性假设上。性能分析表明,2种签名方案在保证安全性的前提下,具有较高的执行效率。     

普适计算环境下的虚拟机技术研究

普适计算环境中资源和服务利用率较低,传统应用程序在该环境下无法兼容。为此,提出一种适用于普适计算环境的虚拟机技术。使用设备请求代理屏蔽复杂的普适计算网络环境,对上层应用提供统一硬件平台,支持多个普适计算应用同时运行。实验结果表明,该虚拟化技术能兼容传统应用,提高普适计算资源和服务的利用率。     

企业服务总线中消息路由器的设计与实现

消息路由器需要解决服务发现与匹配、消息交换并发控制、消息交换生命周期管理等问题。为此,设计一种满足JBI规范的规范化消息路由器,并介绍该路由器的体系结构、消息路由算法以及消息交换状态控制机。给出企业服务总线的应用示例,证明该消息路由器在复杂企业应用集成环境中的有效性、可靠性与安全性。     

基于成对约束和稀疏保留的数据降维算法

结合以成对约束形式给出的监督信息和无监督信息,提出一种基于成对约束和稀疏保留的数据降维算法。通过成对约束信息进行鉴别分析,利用稀疏表示方法保留数据集在变换空间中的全局稀疏结构。实验结果表明,与传统特征抽取算法相比,该算法的识别效果更好,需要调节的参数更少,且鲁棒性较高。     

基于虚拟机日志记录回放的可逆调试方法

传统的调试器调试程序时,仅仅能够让程序正向运行并获取其当前的状态.提出了一种可以让程序逆向运行,回到过去任意时刻的调试方法,来增强调试器的功能.该方法是通过为Xen虚拟机添加完整的日志记录和回放功能以及对GDB调试器作相应修改来实现的;调试对象可以恢复到其运行过程的任意时刻.该可逆调试器,可以解决大型软件和操作系统内核...     

基于虚拟机的运行时入侵检测技术研究

入侵检测技术通常分为误用检测和异常检测两类,误用检测根据攻击模式库检测已知的攻击行为,但却难以防范未知的攻击行为;异常检测技术虽然可以预测偏离正常值阈区间的潜在攻击行为,但却存在较高的误报现象。在虚拟机监视器中对虚拟机操作系统的运行行为进行带外监控,避免了操作系统内监控模块被病毒感染的难题;通过监视虚拟机的运行时行为,对之作组合序列的合法性分析,扩展了误用检测防范长时间段攻击行为的能力,识别通过合法系统调用进行的恶意攻击。测试数据表明,该技术能够较好地检测出复杂组合攻击行为。     

Web-based algorithm for cylindricity evaluation using support vector machine learning

This paper introduces a cylindricity evaluation algorithm based on support vector machine learning with a specific kernel function, referred to as SVR, as a viable alternative to traditional least square method (LSQ) and non-linear programming algorithm (NLP). Using the theory of support vector machine regression, the proposed algorithm in this paper provides more robust evaluation in terms of CPU time and accuracy than NLP and this is supported by computational experiments. Interestingly, it has been shown that the SVR significantly outperforms LSQ in terms of the accuracy while it can evaluate the cylindricity in a more robust fashion than NLP when the variance of the data points increases. The robust nature of the proposed algorithm is expected because it converts the original nonlinear problem with nonlinear constraints into other nonlinear problem with linear constraints. In addition, the proposed algorithm is programmed using Java Runtime Environment to provide users with a Web based open source environment. In a real-world setting, this would provide manufacturers with an algorithm that can be trusted to give the correct answer rather than making a good part rejected because of inaccurate computational results.     

Disc cutters plane layout design of the full-face rock tunnel boring machine (TBM) based on different layout patterns

The quality of plane layout design of the disc cutters for the full-face rock tunnel boring machine (TBM) directly affects the balance of force distribution on the cutter head during the excavating. Various layout patterns have been adopted in practice during the layout design of the disc cutters. Considering the engineering technical requirements and the corresponding structure design requirements of the cutter head, this study formulates a nonlinear multi-objective mathematical model with multiple constraints for the disc cutters plane layout design, and analyses the characteristics of a multi-spiral layout pattern, a dynamic star layout pattern and a stochastic layout pattern. And then a genetic algorithm is employed to solve a disc cutters’ multi-spiral layout problem, and a cooperative co-evolutionary genetic algorithm (CCGA) is utilized to solve a disc cutters’ star or stochastic layout problems. The emphasis was put on the study of superiority of three different layout patterns. Finally, an instance of the disc cutters’ plane layout design was solved by the proposed methods using three different kinds of layout patterns. Experimental results showed the effectiveness of the method of combining the mathematical model with the algorithms, and the pros and cons of the three layout patterns.     

A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity

This paper proposes a two-stage stochastic programming model for the parallel machine scheduling problem where the objective is to determine the machines' capacities that maximize the expected net profit of on-time jobs when the due dates are uncertain. The stochastic model decomposes the problem into two stages: The first (FS) determines the optimal capacities of the machines whereas the second (SS) computes an estimate of the expected profit of the on-time jobs for given machines' capacities. For a given sample of due dates, SS reduces to the deterministic parallel weighted number of on-time jobs problem which can be solved using the efficient branch and bound of M’Hallah and Bulfin [16]. FS is tackled using a sample average approximation (SAA) sampling approach which iteratively solves the problem for a number of random samples of due dates. SAA converges to the optimum in the expected sense as the sample size increases. In this implementation, SAA applies a ranking and selection procedure to obtain a good estimate of the expected profit with a reduced number of random samples. Extensive computational experiments show the efficacy of the stochastic model.