[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123330-en":3,"doc-seo-123330-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},123330,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Ransomware Classification with Machine Learning Algorithms - Thesis","Ransomware growth presents an urgent challenge for the technology sector, requiring fast, reliable identification to reduce monetary loss and ethical harm. Prior detection methods often rely on signatures that cannot generalize to new variants, or use dynamic analysis that is difficult to scale due to complexity and cost. This thesis proposes a feature-selection-based framework that extracts file features and applies multiple machine learning and deep learning models. Experiments compare filter, wrapper, and embedded feature selection combined with DT, RF, NB, LR, SVM, KNN, XGB, and MLP. Results indicate RF and MLP with filter selection achieve stronger accuracy, F-beta, and precision.","CALIFORNIA STATE UNIVERSITY SAN MARCOS  \nTHESIS SIGNATURE PAGE  \nTHESIS SUBMITTED IN PARTIAL FULFILLMENT  \nOF THE REQUIREMENTS FOR THE DEGREE  \nMASTER OF SCIENCE  \nIN  \nCOMPUTER SCIENCE  \nTHESIS TITLE: RANSOMWARE CLASSIFICATION WITH MACHINE LEARNING ALGORITHMS  \nAUTHOR: Torsha Mazumdar  \nDATE OF SUCCESSFUL DEFENSE: 04/12/2023  \nTHE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN  \nPARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE.  \nDr. Nahid Ebrahimi Majd  \nTHESIS COMMITTEE CHAIR  \nDr. Sreedevi Gutta  \nTHESIS COMMITTEE MEMBER  \nSIGNATURE  \nDATE  \nRansomware Classification with Machine Learning Algorithms  \nTable of Contents  \nRansomware Classification with Machine Learning Algorithms ......................................................2  \nABSTRACT ...................................................................................................................................... 4  \nCHAPTER 1: INTRODUCTION ....................................................................................................4  \n1.1 Problem Statement ................................................................................................................. 4  \n1.2 Purpose of the Study and Motivation ...................................................................................4  \n1.3 Research Methodology .......................................................................................................... 4  \n1.4 Research Scope ...................................................................................................................... 5  \nCHAPTER 2: RELATED WORK .................................................................................................. 5  \nCHAPTER 3: METHODOLOGY................................................................................................... 6  \n3.1 Dataset .................................................................................................................................... 6  \n3.2 Data Preprocessing................................................................................................................. 7  \n3.3 Proposed Approach ................................................................................................................ 8  \nCHAPTER 4: ANALYSIS OF THE RESULTS .......................................................................... 12  \n4.1 Evaluation metrics................................................................................................................ 12  \n4.2 Results and Discussion ........................................................................................................ 13  \nCHAPTER 5: CONCLUSION ...................................................................................................... 17  \nReferences ....................................................................................................................................... 18  \nABSTRACT  \nThe rise of ransomware has emerged as a pressing concern for the technology industry, demanding prompt action to prevent monetary and ethical exploitation. Therefore, a fresh approach is imperative to identify and thwart such attacks effectively. Most of the prior detection techniques are either signaturebased which is not sufficient to identify new ransomware or utilized a dynamic analysis which is complicated and computationally expensive. This paper proposes a feature selection-based framework along with different machine learning and deep learning algorithms that can effectively detect ransomware based on features extracted from the files. We performed various experiments beginning with filter, wrapper and embedded methods of feature selection and then applied Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Extreme Gradient Boost (XGB) and Multi-layer Perceptron (MLP) on a ransomware dataset that conta","cbCairqUGUWwOBhT","https://ap.wps.com/l/cbCairqUGUWwOBhT","pdf",1114405,1,19,"English","en",105,"# CHAPTER 1: INTRODUCTION\n## 1.1 Problem Statement\n## 1.2 Purpose of the Study and Motivation\n## 1.3 Research Methodology\n## 1.4 Research Scope\n# CHAPTER 2: RELATED WORK\n# CHAPTER 3: METHODOLOGY\n## 3.1 Dataset\n## 3.2 Data Preprocessing\n## 3.3 Proposed Approach\n# CHAPTER 4: ANALYSIS OF THE RESULTS\n## 4.1 Evaluation metrics\n## 4.2 Results and Discussion\n# CHAPTER 5: CONCLUSION\n# References","[{\"question\":\"What problem does this thesis address?\",\"answer\":\"The thesis targets ransomware detection by explaining how ransomware encrypts files and blocks access until a ransom is paid, making timely identification critical.\"},{\"question\":\"How does the proposed approach detect ransomware?\",\"answer\":\"It uses a feature-selection-based framework to extract features from files and then applies multiple machine learning and deep learning algorithms to classify ransomware.\"},{\"question\":\"Which models and feature-selection method performed best?\",\"answer\":\"The experimental results show that Random Forest (RF) and Multi-layer Perceptron (MLP) combined with the filter method of feature selection outperform other tested combinations in accuracy, F-beta, and precision.\"}]","Ransomware Classification with Machine Learning Algorithms - Thesis | PDF",1785815967,48,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"ransomware-classification-with-machine-learning-algorithms-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/ransomware-classification-with-machine-learning-algorithms-thesis/123330/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does this thesis address?","Question",{"text":76,"@type":77},"The thesis targets ransomware detection by explaining how ransomware encrypts files and blocks access until a ransom is paid, making timely identification critical.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed approach detect ransomware?",{"text":81,"@type":77},"It uses a feature-selection-based framework to extract features from files and then applies multiple machine learning and deep learning algorithms to classify ransomware.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models and feature-selection method performed best?",{"text":85,"@type":77},"The experimental results show that Random Forest (RF) and Multi-layer Perceptron (MLP) combined with the filter method of feature selection outperform other tested combinations in accuracy, F-beta, and precision.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},"General","general"]