[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121476-en":3,"doc-seo-121476-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121476,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Malware Detection System Based on Static and Dynamic Analysis Using Machine Learning","Cyber wars and cyber attacks represent an urgent challenge in the global digital environment, as increasingly sophisticated malware evades existing detection approaches. The study proposes a malware analysis and detection system that combines static and dynamic analysis through machine learning. The model is built on a support vector machine, using multiple data representations and multiple kernel learning. A dataset of 257 executable files (.exe)—178 malicious and 79 benign—supports binary, trace, control-flow graph, dynamic behavior, and file metadata views merged into one classifier.","UDC 004.056  \nMalware Detection System Based on Static and Dynamic Analysis  \nUsing Machine Learning  \nAlan Nafiiev1, Andrii Rodionov1  \n1 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Institute of Physics and Technology, Build. 1, 37, Beresteiskyi avenue, Kyiv, 03056, Ukraine  \nAbstract  \nCyber wars and cyber attacks are an urgent problem in the global digital environment. Based on existing popular detection methods, malware authors are creating ever more advanced and sophisticated malware. Therefore, this study aims to create a malware analysis system that uses both dynamic and static analysis. Our system is based on a machine learning method - support vector machine. The set of data used was collected from various Internet sources. It consists of 257 executable files in .exe format, 178 of which are malicious and 79 are benign. We use 5 different types of data representation: binary information, trace instructions, control flow graph, information obtained from the dynamic operation of the file, and file metadata. Then, using multiple kernel learning, we combine all data views and create one summative machine learning model.  \nKeywords: malware detection, malware dynamic analysis, feature selection, multiple kernel learning  \nIntroduction  \nIn the history of mankind, wars are an integral part of the nature of Homo sapiens. And as the current situation in the world shows, despite the rapid technological breakthrough of the 21st century, war is still inherent in modern man. However, with the growth of the global digitalization of the world, the type of war is also changing. Today we can observe that almost the most important role is played by cyber attacks. Since even one malicious file can harm the vital infrastructure. Therefore, this study is aimed at combating malware, namely the difficult associated with its detection. Malware analysis can be performed by static or dynamic methods. In this work, we use both and combine them.  \nThe aim of this work is to create a binary classification system for executable files. A system that equally well detects both malware with a simple structure and a complex, isomorphic malware that can change its structure during activity. Obviously, to solve such a task, one data view will not be enough. To successfully analyze the true nature of a malicious file, we should extract as much information from it as possible. Therefore, we will use 5 different types of data representation:  \n1. Binary  \n2. Trace  \n3. Control flow graph  \n4. Dynamic  \n5. File Info  \nBinary -uses the byte information contained in the binary executable file. Trace disassembled code is used. These two static methods were described in detail in our previous work [1] . CFG-a method based on control flow graph, the graphlet kernel is used [2] . Dynamica method based on dynamic analysis, where we get information about a file while it is active in a virtual environment [3] . File Info-method based on file metadata. Each method is described in more detail in the section Feature Selection.  \nNow, with the five methods of file representation, the task is to apply this data for classification. For each method, we constructed a machine learning model based on the support vector machine algorithm. This algorithm was chosen because it showed good accuracy results in our work, where we compared different machine learning methods [4] . And the main selection criterion was the fact that SVM uses the properties of kernels, which allows us to apply multiple kernel learning [5, 6] . By means of which several data views can be combined into one model. And this model takes into account the features of each of the methods during training.  \nMalware Detection System Based on Static and Dynamic Analysis Using Machine Learning  \n_________________________________________________________________________________  \nThis approach is described in more detail in the section “Kernel and training”.  \n1. Dataset  \nThe ","cbCaimJ3CLG4zwgj","https://ap.wps.com/l/cbCaimJ3CLG4zwgj","pdf",1331091,1,"English","en",105,"# Introduction\n## Data representation and analysis approach\n## Dataset\n## Feature selection\n### Binary\n### Trace\n### Control flow graph\n### Dynamic analysis\n### File metadata\n## Kernel and training","[{\"question\":\"What is the main goal of the proposed system?\",\"answer\":\"To build a binary classification system for executable files that detects both simple malware and structurally complex, self-changing malware.\"},{\"question\":\"Which machine learning method does the system use?\",\"answer\":\"The system uses support vector machine (SVM) and applies multiple kernel learning to combine information from multiple data views.\"},{\"question\":\"How is the dataset constructed and split?\",\"answer\":\"The dataset includes 257 .exe files (178 malicious, 79 benign) covering seven malware types, split into test and training sets in a 30/70 ratio while keeping the same malware type coverage in both sets.\"}]","Malware Detection System Based on Static and Dynamic Analysis Using Machine Learning | PDF",1785735827,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"malware-detection-system-based-on-static-and-dynamic-analysis-using-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/malware-detection-system-based-on-static-and-dynamic-analysis-using-machine-learning/121476/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the proposed system?","Question",{"text":74,"@type":75},"To build a binary classification system for executable files that detects both simple malware and structurally complex, self-changing malware.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning method does the system use?",{"text":79,"@type":75},"The system uses support vector machine (SVM) and applies multiple kernel learning to combine information from multiple data views.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the dataset constructed and split?",{"text":83,"@type":75},"The dataset includes 257 .exe files (178 malicious, 79 benign) covering seven malware types, split into test and training sets in a 30/70 ratio while keeping the same malware type coverage in both sets.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]