基于虚拟化平台Xen的内核安全监控方案

虚拟化技术作为云计算的基础架构,其安全性随着云计算的发展越来越受到人们的关注.提出一种针对虚拟机系统内核攻击的入侵检测方案,借助Xen提供的虚拟化平台,来获取虚拟机系统内核运行情况,从而达到监视和防止内核被攻击的目的.该方案可以有效地防御来自动态修改内核代码和内核不变数据结构一类的攻击.     

动态网络环境下的链路层拓扑发现算法

完整准确的网络拓扑是网络管理的基础. 由于链路层设备的多样性、透明性、动态性, 因此如何快速、准确地发现链路层拓扑成为近年来研究的热点和难点. 现有的链路层拓扑发现研究均基于静态网络, 然而随着无线网络的普及, 网络中的移动设备越来越多, 静态网络的假设不再成立. 鉴于此, 本文提出了动态链路层拓扑发现算法, 通过在网路中部署有限的agent节点, 发现链路层拓扑. ns-3模拟实验以及对实际运行网络的测量结果表明, 该算法能有效排除移动设备的影响, 快速准确地发现链路层拓扑结构.     

Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics

Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.     

HEMD: a highly efficient random forest-based malware detection framework for Android

Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and poses a serious security threat. Google’s Android is the most popular smart phone platforms in the world and the mechanisms of permission declaration access control cannot identify the malware. In this paper, we proposed an ensemble machine learning system for the detection of malware on Android devices. More specifically, four groups of features including permissions, monitoring system events, sensitive API and permission rate are extracted to characterize each Android application (app). Then an ensemble random forest classifier is learned to detect whether an app is potentially malicious or not. The performance of our proposed method is evaluated on the actual data set using tenfold cross-validation. The experimental results demonstrate that the proposed method can achieve a highly accuracy of 89.91%. For further assessing the performance of our method, we compared it with the state-of-the-art support vector machine classifier. Comparison results demonstrate that the proposed method is extremely promising and could provide a cost-effective alternative for Android malware detection.

     

果糖在面包、蛋糕中的应用

<正>果糖是一种新发展的甜味剂,具有甜度高、风味好、吸湿性强等特点。果糖是以淀粉为原料,经酶解而转化为较甜的果糖。国外使用果糖代替部分蔗糖广泛用于甜酒、汽水、冰琪琳、面包、糕点、果脯、果酱等食品工业中,成为重要的甜味剂之一。     

基于PIC 单片机的超声测距报警系统

本文设计了一种基于PIC16F877A的超声测距报警系统,通过温度补偿、软件滤波以及提高发射驱动电压的方法,解决传统测距系统测量距离较近的问题,提高了探测距离和测量的精准度,并实现了实时报警的功能;解释了CX20106A芯片输出信号电平变化的原因,提出了两种报警思路并对其进行了验证和实现。实验结果表明该系统能很好的满足安防监控的需求。     

RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences

Protein-Protein Interactions (PPIs) play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed a freely available web server called RVMAB-PPI in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/ppi_ab/.     

Xen硬件虚拟化下一种客户机进程追踪技术

Xen硬件虚拟化(HVM)是运行于Xen Hypervisor上, 无需修改客户操作系统内核便可实现虚拟化的技术. 但Xen HVM在提升用户体验的同时, 也造成了VMM对客户机的干涉和理解程度的降低, 丧失了进程粒度级别的管理能力. 鉴于此, 提出了一种针对Xen HVM客户机的轻量级进程追踪技术原型Xen HPT. 借助VMM掌握的客户机ESP寄存器信息, 从客户机外部隐式捕获其实时进程信息. 实验证明, 该技术是一种追踪客户机进程的有效方法.     

Sample bound estimate based chance-constrained immune optimization and its applications

This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.