[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121559-en":3,"doc-seo-121559-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121559,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Performance Metrics of Different Machine Learning Models for Windows Malware Detection - Research Overview","This study experimentally evaluates and analyzes the performance of multiple machine learning models for Windows malware detection, using a dataset composed of known Windows malware samples and benign files. Logistic Regression, AdaBoost, LightGBM, XGBoost, Decision Trees, Gradient Boosting, Bagging, Random Forest, and Support Vector Machines are assessed with metrics including accuracy, precision, recall, F1 score, specificity, false positive/negative rates, NPV, and error rate. Feature selection and extraction are also examined. Results show performance differences across models, highlighting that Random Forest, Bagging XGB, and LGBM excel on specific metrics, supporting stronger cybersecurity strategy development.","Performance Metrics of Different Machine Learning Models for Windows Malware Detection  \nFadhil Mukhlif1*, Ibrahim Hashem2, Norafida Ithnin3  \n1 Department of Cybersecurity Engineering Techniques, Technical Engineering College for Computer and AI, Northern Technical University, Kirkuk, IRAQ  \n2 Department of Computer Science, College of Computing and Informatics,  \nUniversity of Sharjah, Sharjah, UNITED ARAB EMIRATE  \n3 Information Assurance and Security Research Group (IASRG), Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor MALAYSIA  \n*Corresponding Author: [fmukhlif@ntu.edu.iq](fmukhlif@ntu.edu.iq)  \nDOI: [https://doi.org/10.30880/jaita.2025.06.02.004](https://doi.org/10.30880/jaita.2025.06.02.004)  \nArticle Info  \nReceived: 23 September 2025  \nAccepted: 3 November 2025  \nAvailable online: 31 December 2025  \nKeywords  \nCybersecurity, malware detection, machine learning, AI models, performance metrics  \nAbstract  \nThis study experimentally evaluates and analyzes the performance of various machine learning models for Windows malware detection. Their metrics are further analyzed to identify the most effective approach. For this purpose, the researchers employed a diverse dataset to train and assess the models. The used dataset contains known Windows malware samples and benign files. Besdies, the chosen machine learning algorithms, such as Logistic Regression (LR), AdaBoost, LightGBM (LGBM), XGBoost (XGB), Decision Trees (DT), Gradient Boosting, Bagging, Random Forest (RF), and Support Vector Machines (SVM), have various techniques. The study focuses on key performance metrics: Accuracy, Precision, Recall, F1 Score, Specificity, False Positive Rate (FPR), Negative Predictive Value (NPV), False Negative Rate (FNR), and Error Rate. They are used to thoroughly assess the models' effectiveness in distinguishing between malware and benign samples. Additionally, the exploration of the impact of feature selection and extraction methods on model performance is carried out to gain better insights. The study results demonstrate variations in the models' effectiveness. It is noted that certain algorithms like; random Forest, Bagging XGB, and LGBM are demonstrate superior performance in specific metrics. They also offer significant perspectives into the strengths and weaknesses of various machine learning models in the detection of Windows malware, contributing valuable knowledge to the development of more robust cybersecurity strategies. The study implications can hopefully be used to develop an effective and accurate malware detection model. It is expected the model may ultimately foster the security of Windows environments.  \n1. Introduction  \nWith the evolving and shifting fields of cybersecurity, Windows malware detection and mitigation represent the cutting edge of protecting digital environments [1]. As malicious software becomes more sophisticated and diverse, there is a dire need for effective machine learning models to identify and neutralize these threats [2]. Fig.  \n1 illustrates a scenario where hackers perform various malware attacks on systems. The increase of machine learning algorithms has emerged recently in fostering the malware detection systems in terms of efficiency and  \naccuracy. Threats posed by Internet-based malware are discussed in [3], along with the shortcomings of human heuristic analysis in slowing down its spread. The author’s proposed solution involves automated behavior-based malware detection utilizing machine learning. Malware behavior is scrutinized in a simulated environment, generating reports subsequently processed into sparse vector models for classification. Moreover, [4] build a malware detection system to be accessible online which on process-level performance metrics.  \nThe study has tested the effectiveness of various techniques such as KNN, SVC, RFC, GNB and CNN. Utilizing adataset comprising both malicious and benign samples, the research concludes that neural network ","cbCais7MUYC3LamV","https://ap.wps.com/l/cbCais7MUYC3LamV","pdf",922764,1,11,"English","en",105,"# Introduction\n# Methodology and Models\n## Performance Metrics\n## Feature Selection and Extraction\n# Experimental Results and Discussion\n# Conclusion","[{\"question\":\"Which models show superior performance according to the results?\",\"answer\":\"The results indicate that Random Forest, Bagging XGB, and LGBM demonstrate superior performance on specific metrics, reflecting different strengths across models.\"}]","Performance Metrics of Different Machine Learning Models for Windows Malware Detection - Research Overview | PDF",1785736240,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"performance-metrics-of-different-machine-learning-models-for-windows-malware-detection-research-overview","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/performance-metrics-of-different-machine-learning-models-for-windows-malware-detection-research-overview/121559/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Which models show superior performance according to the results?","Question",{"text":75,"@type":76},"The results indicate that Random Forest, Bagging XGB, and LGBM demonstrate superior performance on specific metrics, reflecting different strengths across models.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]