Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.

Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.

Fast Face Detection with Multi-Scale Window Search Free from Image Resizing Using SGI Features

Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.

一种Windows主机入侵检测实验系统

针对广泛使用的Windows平台,建立了一个基于主机的入侵检测实验系统。在深入分析Windows主机的安全特性的基础上,利用安全日志、系统日志、性能日志及文件完整性校验、注册表等多种信息,提出了18项入侵检测特征,并利用支持向量机建立入侵检测器,实现了对多种攻击的检测。实验结果表明,特征选取合理、检测方法有效。

基于ARM & Windows CE的锚杆无损检测系统软件设计

设计了一款基于ARM & Windows CE及FFT算法的锚杆无损检测系统,介绍了硬件电路设计方法,包括信号采集模块、A/D转换模块、液晶显示模块、电源模块;在软件设计中,完成了Windows CE操作系统的定制以及应用程序设计,并对设计方法和数据FFT分析方法进行了阐述。

MS Windows定时器性能分析及在定时-计数系统设计中的应用

通过对MSWindows定时器性能的测试和分析,利用多媒体定时器精度分布的特点,采用"合成定时-选择计数"方法获得预定精度定时,在放射测量定时-计数系统中应用。

基于支持向量机的Windows主机入侵检测系统

提出了一种基于支持向量机的W indow s主机入侵检测方法。讨论了以W indow s注册表作为数据源的入侵检测系统的结构及特征向量的提取方法。给出了基于支持向量机的入侵分类算法,通过建立支持向量描述模型进行预测。实验表明:该方法对已知样本有很高的检测率,对未知样本也有一定检测能力。

Windows RootKit检测与取证技术研究

文章首先对近几年来Windows RootKit检测技术的发展和研究进行了描述,接着对当前常用的RootKit检测工具及其检测方法做了介绍,进而探讨了Windows RootKit的取证与分析方法,以期对当前计算机取证人员有效解决Windows RootKit检测和取证提供一些借鉴。

基于Windows的文件完整性检测系统的设计和实现

有些计算机病毒和木马一旦进入系统,就会伪装自己,使系统难以觉察。通过对主机文件系统的完整性验证,能有效地拦截系统未 知的病毒和木马。在Windows系统中,使用拦截技术,当可执行程序运行时对其进行拦截,并验证其文件完整性,确保程序未受感染后,才 允许程序执行。利用文件完整性检查防范计算机病毒和木马,具有较好的实时性和主动性。

Windows环境下的通用温度检测软件系统设计

介绍了Windows环境下的通用温度检测软件系统设计。在设计中,遵循通用检测软件设计思想和有效地利用windows平台提供的技术支持,构造了合理的体系结构。生成系统与运行系统相互分离,并通过数据字典有机结合的安装 运行结构,使系统具有良好的通用性;同时,利用定时+消息机制圆满地解决了公用数据区的共用冲突,使用事务存取解决了温度数据库的共享。

基于支撑矢量机和Windows Native API的异常检测方法

借鉴Unix类系统下基于系统调用的主机异常检测理论,通过追踪Windows本机应用编程接口调用序列,对Windows系统下的主机异常检测进行研究.在异常序列检测中,结合使用对小数据集具有较好推广能力的支撑矢量机方法,进而取得较高的检测准确率.实验表明Native API可为Windows平台下基于主机的异常检测系统提供一种可能的数据源.

基于Windows 2000 Server自身功能及脚本的入侵检测

阐述了利用Windows 2 0 0 0Serv er自身的功能及系统管理员自己编写的软件 /脚本实现安全检测。初步探讨Windows 2 0 0 0服务器入侵检测的初步技巧以及如何长期维护服务器的安全。

基于Windows CE平台的水准桥梁几何检测系统的开发

基于Windows CE操作平台的水准仪桥梁几何检测及数据采集处理系统,能够完成桥梁检测中的基准点检测、墩台沉降观测、桥梁挠度观测以及桥面线形检测的全数字化作业,通过远程数据传输可实现桥梁检测内外业的一体化,带来巨大的经济效益和社会效益。

基于Windows NativeAPI序列的系统行为入侵检测

针对Windows系统入侵检测的不足,研究并借鉴Linux下基于系统调用序列进行入侵检测的方法,提出一种采用BP神经网络算法对Windows Native API序列学习和分类的内核级主机入侵检测方案。通过实验,验证了采用Windows Native API序列进行系统入侵的可行性。Native API是Windows系统内核模式下的API,可以类比于Linux下的系统调用。通过训练神经网络学习Native API序列,建立一个对正常和异常Native API序列进行分类的BP神经网络。在入侵检测时,利用训练后的神经网络对不断出现的Windows Native API序列进行分类,判断系统是否出现异常入侵。

Windows Mobile的智能终端上内存泄露检测研究

Windows Mobile 5.0是微软为智能移动终端推出的软件平台,特别在智能手机领域受到越来越广泛的应用;在基于Windows Mobile的产品设计中,内存泄露又是需要考虑的关键之一。文中解析了Windows Mobile平台上监测设备内存泄露的工具AppVerifier,并在模拟器上实现了监测应用程序内存泄露,进而发现使用AppVerifier存在的问题,并指出了使用中高效利用AppVerifier的方法。

基于Windows平台的车载式土壤全氮快速检测系统软件

为了建立一套土壤全氮含量在线快速测量方法,基于Windows平台设计了车载式土壤全氮快速检测系统软件,系统软件设计包括软件架构设计和软件功能设计。在Windows平台上使用MySQL数据库管理软件作为检测系统软件数据库进行设计,实现检测系统软件与MySQL数据库的数据交互;软件功能设计主要包括检测系统软件主界面设计、数据采集、分析界面设计,以及利用HTML5地理位置定位技术实现土壤数据检测点的轨迹成图界面设计。当车载式土壤全氮快速检测系统进行土壤检测时,系统软件采集不同敏感波长处的土壤光谱数据,通过嵌入在检测系统软件中的土壤全氮含量预测模型,反演得到土壤全氮含量信息,同时采集的GPS信息通过HTML5地理位置定位技术实现土壤数据检测点的轨迹成图。最后,对检测系统软件进行了测试,测试结果表明,车载式土壤全氮快速检测系统软件能够有效采集和显示不同敏感波长处的土壤光谱信息、土壤全氮含量及GPS信息,并能够准确生成土壤数据检测点的轨迹图,证明了基于Windows平台的车载式土壤全氮快速检测系统软件的可靠性和稳定性,能够满足田间在线、快速检测土壤全氮含量及生成土壤数据检测点轨迹图的需求。

基于Windows CE的嵌入式焊接质量在线监测设备的研究

文章针对机器人短路GMAW焊接过程的在线质量监测 ,通过检测过程中的焊接电流和电压 ,采用基于WindowsCE操作系统的嵌入式便携设备 ,实现对焊接质量的在线监测。以焊接过程电参数的分析和实验为基础 ,介绍了该监测设备的开发步骤及取得的一些结果。在满足PC机具有的人机界面的基础上 ,它还比传统的基于PC的系统具有稳定性好、可靠性高、成本低等优点 。

利用Windows Native API调用序列和基于决策树算法的主机异常检测

主要研究W indows平台下的异常检测方法,提出一种利用W indows Native API调用序列和基于决策树算法的主机服务进程模式抽取算法,并通过在模式中引入通配符而大大缩减了模式集的规模。进一步引入了表征模式间关系的转移概率,建立了模式序列的全局马尔可夫链模型,并给出了相应的异常检测算法。实验结果表明:该算法可以抽取一个规模较小且泛化能力较强的模式集,相应的检测算法可以有效地检测异常。