[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121248-en":3,"doc-seo-121248-105":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},121248,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","MALWD&C - A Quick and Accurate Machine Learning-Based Approach for Malware Detection and Categorization","Malware—malicious software—poses serious risks to computer systems and networks, motivating effective prevention of harmful installation. This work presents MALWD&C, a machine learning-based approach that analyzes Portable Executable (PE) files and classifies them as benign or malware. Experiments on a public dataset evaluate multiple classifiers in two-class detection and multi-class categorization settings. Results show Random Forest achieves up to 99.56% (two-class) and 97.69% (multi-class) accuracy, emphasizing speed and improved reliability for academia and industry.","applied sciences  \nArticle  \nMALWD&C: A Quick and Accurate Machine Learning-Based Approach for Malware Detection and Categorization  \nAttaullah Buriro 1, Abdul Baseer Buriro 2, Tahir Ahmad 3, *, Saifullah Buriro 4 and Subhan Ullah 5  \nCitation: Buriro, A.; Buriro, A.B.; Ahmad, T.; Buriro, S.; Ullah, S. MALWD&C: A Quick and Accurate Machine Learning-Based Approach for Malware Detection and Categorization. Appl. Sci. 2023, 13, 2508. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app13042508  \nAcademic Editors: Konstantinos Rantos, Konstantinos Demertzis and George Drosatos  \nReceived: 13 January 2023  \nRevised: 3 February 2023  \nAccepted: 13 February 2023  \nPublished: 15 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Computer Science, Free University Bozen-Bolzano, 39100 Bolzano, Italy  \n2 Electrical Engineering Department, Sukkur IBA University, Sukkur 65200, Pakistan  \n3 Center for Cybersecurity, Brunno Kessler Foundation, 38123 Trento, Italy  \n4 Faculty of Engineering Science & Technology, Deparment of Electrical Engineering, Indus University Karachi, Karachi 75500, Pakistan  \n5 Department of Computer Science, National University of Computer and Emerging Sciences (NUCES-FAST), Islamabad 44000, Pakistan  \n* Correspondence: [ahmad@fbk.eu](ahmad@fbk.eu)  \nAbstract: Malware, short for malicious software, is any software program designed to cause harm toa computer or computer network. Malware can take many forms, such as viruses, worms, Trojan horses, and ransomware. Because malware can cause significant damage to a computer or network, it is important to avoid its installation to prevent any potential harm. This paper proposes a machine learning-based malware detection method called MALWD&C to allow the secure installation of Programmable Executable (PE) files. The proposed method uses machine learning classifiers to analyze the PE files and classify them as benign or malware. The proposed MALWD&C scheme was evaluated on a publicly available dataset by applying several machine learning classifiers in two settings: two-class classification (malware detection) and multi-class classification (malware categorization) . The results showed that the Random Forest (RF) classifier outperformed all other chosen classifiers, achieving as high as 99.56% and 97.69% accuracies in the two-class and multi-class settings, respectively. We believe that MALWD&C will be widely accepted in academia and industry due to its speed in decision making and higher accuracy.  \nKeywords: malware detection and categorization; pattern matching; binary and multi-class classification  \n1. Introduction  \nCurrently, computers are playing an unavoidable and omnipresent role in our daily lives. They are used for various reasons: storing data, performing research, gaming, social networking, and entertainment, to name a few. However, they are facing continuous, ever-increasing, and constantly evolving cyber security threats. Therefore, it is crucial to implement effective and efficient security measures to protect computers and the sensitive data they contain. One such measure is the use of machine learning-based malware detection methods, such as MALWD&C, to identify and prevent the installation of malicious software on computers. By leveraging the power of machine learning algorithms, these methods can quickly and accurately classify files as benign or malware, allowing users to install only safe and secure software on their computers. In addition to protecting against cyber security threats, machine learning-based malware detection methods can also help organizations comply with regulations and standards, su","cbCainqAaE4hOGC5","https://ap.wps.com/l/cbCainqAaE4hOGC5","pdf",708251,1,14,"English","en",105,"# Introduction\n## Threat landscape and need for protection\n## Malware injection and Windows PE focus\n## Existing detection approaches\n## Proposed MALWD&C overview","[{\"question\":\"What problem does MALWD\\u0026C address?\",\"answer\":\"MALWD\\u0026C targets the prevention of malicious installation by detecting and categorizing malware in Portable Executable (PE) files for Windows systems.\"},{\"question\":\"How does MALWD\\u0026C work?\",\"answer\":\"It uses machine learning classifiers to analyze PE files and assign them to benign or malware, and also supports multi-class malware categorization.\"},{\"question\":\"Which classifier performed best and what accuracy was reported?\",\"answer\":\"Random Forest outperformed other classifiers, reaching up to 99.56% accuracy for two-class malware detection and 97.69% accuracy for multi-class categorization.\"}]","MALWD&C - 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