Using artificial neural networks to detect unknown computer worms

Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN’s ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.     

机器更新对蠕虫传播影响

对机器的更新换代对蠕虫传播、演化等动力学行为的影响进行了研究,给出了一个蠕虫传播模型,运用动力学分析方法得到了模型的平衡态及其稳定性条件,并进行了仿真验证。理论分析与仿真结果均表明,机器更新会导致蠕虫的持续。仿真结果还表明,机器更换率对蠕虫的传播具有双重影响。在基本再生数的指导下,通过提高防病毒系统预装率,能减弱机器更新带来的负面影响,并能有效抑制蠕虫的传播。     

基于Delphi7.0的上位机与PLC的通信

本文介绍在将计算机与PLC结合起来,组成高性能价格比的小型集散控制系统―热电厂循环水过程控制系统中,在基于Delphi7.0上位机监控软件开发环境中如何实现上位机与S7-200系列PLC之间通信。     

基于计算机图像获取不同产区复烤片烟颜色的探索

对于工业企业而言,了解复烤片烟的颜色有利于指导烟叶储存养护等技术工作的开展。为了解决在复烤片烟环节颜色区分的局限,使烟叶颜色外观质量分析评价更为科学、准确,通过计算机图像获取技术,采用CIE1976L~*a~*b~*均匀色空间系统,并对数据进行PCA分析。结果表明:与传统物理或化学判断方法比较,该方法能有效地对河南、吉林、四川3个产区的正面、背面、混合面进行颜色区分,是一种可靠的复烤片烟质量评价辅助手段。     

基于WPF的电能质量检测系统上位机软件设计

电能质量直接影响着电力系统能否安全运行,为了能及时可靠地检测电能质量,采用全新的Windows Presentation Foundation(WPF)技术,设计了一款电能质量检测系统上位机软件。从工业控制上位机软件的特点出发,实现了上位机与下位机的串口通信,绘制了实时数据波形图及谐波波形图,完成了检测数据的清晰显示。该系统人机界面美观,操作方便,数据存储量大,功能完善。     

基于机器视觉的驾驶员换道行驶检测研究

为自动检测驾驶员行车路线,通过车载CCD图像传感器获得序列图像,利用计算机视觉技术,建立摄像机的透视投影模型和汽车换道行驶检测模型,应用改进的Hough变换识别道路标线,确立直线方程,采用逆透视投影变换对车辆在当前位置的横向距离和横向偏转角做出估计,进而对汽车行驶状态做出判断。实验证明,该方法能够正确检测和判断驾驶员是否处于压线行驶和换道行驶状态。     

The effect of emotional feedback on behavioral intention to use computer based assessment

This study introduces emotional feedback as a construct in an acceptance model. It explores the effect of emotional feedback on behavioral intention to use Computer Based Assessment (CBA). A female Embodied Conversational Agent (ECA) with empathetic encouragement behavior was displayed as emotional feedback. More specifically, this research aims at investigating the effect of Emotional Feedback on Behavioral Intention to Use a CBA system, Perceived Playfulness, Perceived Usefulness, Perceived Ease of Use, Content and Facilitating Conditions. An appropriate survey questionnaire was completed by 134 students. Results demonstrate that Emotional Feedback has a direct effect on Behavioral Intention to Use a CBA system and on other crucial determinants of Behavioral Intention. Finally, the proposed acceptance model for computer based assessment extended with the Emotional Feedback variable explains approximately 52% of the variance of Behavioral Intention.     

基于c#和HostLink的PLC与PC机通信实现

文章对欧姆龙CS系列PLC的HostLink通信协议进行了介绍,并提出了使用c#利用Visual Studio2005中的SerialPort控件实现PLC与上位机通信的方法,给出了实现通信的具体步骤以及优化的通信方案,这种通信方案保证了通信的准确性,提高了通信的实时性。通过上位机对PLC的读写,实现了对工业现场数据的自动采集,可以有效地对工业现场设备进行监控和管理。编写的串口通信软件在实际项目中得到了成功应用,验证了它的可靠性和稳定性。     

基于软件基因的Android恶意软件检测与分类

随着移动互联网的发展,针对Android平台的恶意代码呈现急剧增长。而现有的Android恶意代码分析方法多聚焦于基于特征对恶意代码的检测,缺少统一的系统化的分析方法,且少有对恶意代码分类的研究。基于这种现状,提出了恶意软件基因的概念,以包含功能信息的片段对恶意代码进行分析;基于Android平台软件的特点,通过代码段和资源段分别提取了软件基因,其中代码段基因基于use-def链（使用-定义链）进行形式化。此外,分别提出了基于恶意软件基因的检测框架和分类框架,通过机器学习中的支持向量机对恶意软件基因进行学习,有较高的检测率和分类正确率,其中检测召回率达到了98.37%,验证了恶意软件基因在分析同源性中的作用。     

Detection and classification of non-linearities based on Volterra kernels processing

Modelling and identification of the real systems, which are more or less non-linear, are important activities not only for scientific research but also for real-time applications like fault detection and compensation. In this paper the non-linear system is defined by a serial combination of a linear part followed by a variable static parameterised non-linearity. A structure to detect and classify some non-linearities based on Volterra kernels is proposed. Such kernels can be estimated from the I–O measurements of the system under study. Processing Volterra kernels means estimation followed by classification. Mainly artificial neural networks to obtain a fast answer process the Volterra kernels. The neural methods and the detection structure can be developed to study more complicated non-linear models for fault detection purposes and structural identification of complex non-linear systems. The feasibility of the method is demonstrated using a simulated example.     

基于改进的YOLOv3-DN对脑胶质瘤检测分级

探讨一种基于改进的YOLOv3-DN计算机辅助诊断系统对脑部胶质瘤核磁共振成像术前检测和分级的价值.根据收集的低级别胶质瘤30例和多形性胶质母细胞瘤30例,对其进行数据增强,通过N4 ITK偏场校正及归一化预处理后,将2400幅图像用于改进的YOLOv3-DN网络进行训练,后在测试数据集上检测,通过5折交叉验证及ROC...     

计算机安全漏洞分类研究

计算机及网络安全问题的根源在于计算机漏洞的存在。漏洞是实施网络攻击和加强网络防护的关键因素,漏洞的分类研究是漏洞研究的基础。该文首先给出了计算机安全漏洞的定义并分析了漏洞分类的重要意义,然后介绍了典型的漏洞分类方法和目前常用的漏洞分类法,在此基础上,从多维分类方式和动态变化方式上提出了漏洞分类的进一步研究方法。     

The behavioral sciences in the computer age

This article proposes that computers are likely to have a major impact on our society in general, and on psychology, psychiatry, sociology and related behavioral sciences in particular. It is argued that the continuing technological improvements being made on computers and their increasingly widespread use in society is likely to affect behavioral scientists in two ways. First, they themselves will become users of computers. Second, they will be called upon to evaluate and help control how computers impact human behavior. Ways of proactively shaping such future developments are discussed.     

基于计算机视觉的中药饮片分类技术综述与案例研究

中药饮片相似品、真伪、变质等方面的分类对临床中药应用有着至关重要的影响。传统的人工鉴别方法主观性强、误判率高,而基于计算机视觉的中药饮片分类具有快速、准确等特点,使饮片筛选智能化。首先,介绍了基于计算机视觉的中药识别算法的一般步骤,并分类综述了对中药图像的预处理、特征提取和识别模型的技术发展现状;然后,选取12类相似易混中药饮片分类作为案例研究。通过构建包含9 156张图片的中药饮片数据集,分析对比了传统识别算法和多种深度学习模型的识别性能差异;最后,对计算机视觉在中药饮片应用研究中存在的难点和未来发展趋势进行了总结和展望。     

基于自由口模式的S7-200 PLC与上位机的通信

详细说明了西门子S7-200 PLC在自由口模式下与上位机通信的实现,主要包括该系统的硬件构成,自由口通信协议的标准,以及上位机软件流程和主控PLC软件设计。通过自由口通信可实现对ITER导体穿缆测力测长系统的监控,将PLC数据传送至上位机,实现对数据的处理以及现场数据的实时显示和远程控制等功能。     

Bayesian classification for data from the same unknown class

In this paper, we address the problem of how to classify a set of query vectors that belong to the same unknown class. Sets of data known to be sampled from the same class are naturally available in many application domains, such as speaker recognition. We refer to these sets as homologous sets. We show how to take advantage of homologous sets in classification to obtain improved accuracy over classifying each query vector individually. Our method, called homologous naive Bayes (HNB), is based on the naive Bayes classifier, a simple algorithm shown to be effective in many application domains. RNB uses a modified classification procedure that classifies multiple instances as a single unit. Compared with a voting method and several other variants of naive Bayes classification, HNB significantly outperforms these methods in a variety of test data sets, even when the number of query vectors in the homologous sets is small. We also report a successful application of HNB to speaker recognition. Experimental results show that HNB can achieve classification accuracy comparable to the Gaussian mixture model (GMM), the most widely used speaker recognition approach, while using less time for both training and classification.     

Screening for diabetic retinopathy using computer based image analysis and statistical classification

Diabetic retinopathy is one of the most common causes of blindness in Europe. However, efficient therapies do exist. An accurate and early diagnosis and correct application of treatment can prevent blindness in more than 50% of all cases. Digital imaging is becoming available as a means of screening for diabetic retinopathy. As well as providing a high quality permanent record of the retinal appearance, which can be used for monitoring of progression or response to treatment, and which can be reviewed by an ophthalmologist, digital images have the potential to be processed by automatic analysis systems. We have described the preliminary development of a tool to provide automatic analysis of digital images taken as part of routine monitoring of diabetic retinopathy in our clinic. Various statistical classifiers, a Bayesian, a Mahalanobis, and a KNN classifier were tested. The system was tested on 134 retinal images. The Mahalanobis classifier had the best results: microaneurysms, haemorrhages, exudates, and cotton wool spots were detected with a sensitivity of 69, 83, 99, and 80%, respectively.     

基于支持向量机的脑-机接口模式分类和模型参数研究

脑—机接口（BCI）是连接大脑和计算机及外部设备的通讯系统,通过连续小波变换（CWT）对采集的脑电信号进行分解,构造由多个尺度对应的方差构成的多维向量,应用支持向量机（SVM）进行分类识别,取得了良好的效果。基于统计学习理论的结构化风险最小化原则,研究了高斯核支持向量机误差惩罚参数C和高斯核参数σ对支持向量机性能的影响,使用仿真实验验证了传统的经验风险最小化原则不能保证良好的推广能力,提出了综合调整参数σ和参数C的方法以优化支持向量机的性能。     

基于热管技术的矿井透地通信主机散热结构设计

为解决透地通信系统主机散热问题,基于热管技术设计了矿井透地通信主机散热结构,采用数值计算方法研究了热管散热结构传递热阻,结果表明,该散热结构能满足主机散热性能要求;采用数值仿真得到了热管散热结构的温度场云图,结果与数值计算十分吻合。样机温度监测试验验证了设计的正确性。