[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120231-en":3,"doc-seo-120231-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120231,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Comparative Analysis of Dimensionality Reduction on Ransomware Detection Using Machine Learning Techniques","Ransomware attacks continue to evolve as a pervasive cybersecurity threat, causing data loss, financial losses, and disruption of critical services, which increases the need for robust detection mechanisms. Machine learning-based ransomware detection has gained traction, yet high-dimensional feature spaces can reduce model efficiency and effectiveness. This study evaluates two dimensionality reduction methods, LDA and PCA, applied to a Ransomware Portable Executable Header Feature dataset (1028 features) across five classifiers (KNN, DT, RF, SVM, NB) using Accuracy, Recall, Precision, and F1-Score to compare performance outcomes.","LAUTECH Journal of Engineering and Technology 18 (4) 2024: 23-33  \n10.36108/laujet/4202.81.0430  \nComparative Analysis of Dimensionality Reduction on Ransomware Detection Using Machine Learning  \nTechniques  \n1Akinola Olaoluwa, *2Amusan Elizabeth and 3Adeosun Olajide  \n1Department of Computer Science, Ladoke Akintola University of Technology, Ogbomoso; [sotm13@gmail.com](sotm13@gmail.com)[ ](sotm13@gmail.com)2Department of Cyber Security Science, Ladoke Akintola University of Technology, Ogbomoso;  \n[eaadewusi@lautech.edu.ng](eaadewusi@lautech.edu.ng)  \n3Department of Computer Science, LadokeAkinola University of Technology, Ogbomoso:  \n[ooadeosun@lautech.edu.ng](ooadeosun@lautech.edu.ng)  \n\n| Article Info |  ABSTRACT  Ransomware attacks continue to evolve as a pervasive threat to cybersecurity such as data loss, financial losses, and potential disruption of critical services which have prompted the need for robust detection mechanisms. Leveraging on machine learning techniques for ransomware detection has gained recognition; however, the high-dimensional nature of feature spaces has posed some challenges in model efficiency and effectiveness. This research therefore explores the impact of two well-known dimensionality reduction methods that may enhance ransomware detection using five popularly used machine learning algorithms which are KNearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and Naive Bayes (NB). Through comprehensive analysis and experimentation, two well-known dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) were examined on the selected machine learning algorithms using a Ransomware Portable Executable Header Feature Dataset publicly available on an online data repository with URL [https://data.mendeley.com/datasets/p3v94dft2y/2 with](https://data.mendeley.com/datasets/p3v94dft2y/2 with) 1028 features. Metrics such as Accuracy, Recall, Precision and F1-Score were used to evaluate the classifiers. The comparative analysis of LDA and PCA revealed a discernible preference for one classifier over another. From the results, it was observed that the performance of classifiers with PCA was better than that of with\u003Cbr>LDA. Also, Decision Tree and Random Forest classifiers outperform the other three algorithms without using dimensionality reduction as well as with both PCA and LDA. |\n| --- | --- |\n| Article history:\u003Cbr>Received: Sept. 17, 2024\u003Cbr>Revised: Oct. 25, 2024\u003Cbr>Accepted: Nov. 3, 2024 |  |\n| Keywords:\u003Cbr>Dimensionality Reduction, Features, Machine Learning,\u003Cbr>Ransomware Detection\u003Cbr>.\u003Cbr>Corresponding Author:\u003Cbr>[eaadewusi@lautech.edu](eaadewusi@lautech.edu). ng |  |\n\nINTRODUCTION  \nRansomware is a type of malware from cryptovirology that threatens to publish the victim’s data or perpetually block access to it unless a ransom is paid. Ransomware’s main objective is extortion by imposing some form of denial of service to either the system or system resources such as files until a ransom is paid. This makes ransomware different from conventional malware that seeks to replicate,  \ndelete files, exhilarate data or extensively consume system resources (Urooj et al., 2021). While some simple ransomware may lock the system so that it isnot difficult for a knowledgeable person to reverse it, more advanced malware uses a technique called cryptoviral extortion (Alraizza and Algarn (2023) . In a properly implemented cryptoviral extortion attack, recovering the files without the decryption key is an intractable problem. Digital currencies  \nsuch as Paysafecard or bitcoin and other cryptocurrencies are used for the ransoms. This makes tracing and prosecuting the perpetrators difficult.  \nIn the past few decades, numerous dimensionality reduction techniques have been used for filtering the data samples ofthe considered dataset. Reduction of dimensionality requires mapping of inputs that are of high dimensionality to a le","cbCainyfkUjhHAcN","https://ap.wps.com/l/cbCainyfkUjhHAcN","pdf",589834,1,11,"English","en",105,"# Introduction\n## Ransomware background and threat model\n## Dimensionality reduction motivation\n## Machine learning for ransomware detection\n## Study scope and evaluation setup","[{\"question\":\"Which dimensionality reduction methods are compared for ransomware detection?\",\"answer\":\"The paper examines Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) for improving ransomware detection models.\"},{\"question\":\"Which machine learning classifiers are evaluated in the study?\",\"answer\":\"Five classifiers are tested: K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB).\"},{\"question\":\"What metrics are used to evaluate the classifiers’ performance?\",\"answer\":\"The study uses Accuracy, Recall, Precision, and F1-Score to assess and compare the classifiers under different dimensionality reduction settings.\"}]","Comparative Analysis of Dimensionality Reduction on Ransomware Detection Using Machine Learning Techniques | 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dimensionality reduction methods are compared for ransomware detection?","Question",{"text":75,"@type":76},"The paper examines Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) for improving ransomware detection models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning classifiers are evaluated in the study?",{"text":80,"@type":76},"Five classifiers are tested: K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB).",{"name":82,"@type":73,"acceptedAnswer":83},"What metrics are used to evaluate the classifiers’ performance?",{"text":84,"@type":76},"The study uses Accuracy, Recall, Precision, and F1-Score to assess and compare the classifiers under different dimensionality reduction 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