[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119888-en":3,"doc-seo-119888-105":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":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},119888,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345",8,"Research & Report","Machine Learning for malware detection and classification - Master thesis","Machine Learning for malware detection and classification studies how supervised models can identify malicious software and distinguish families using benchmark datasets. The thesis designs a methodology covering training and evaluation workflows, then compares multiple algorithms including Naive Bayes, K-Nearest Neighbours, Decision Tree, Random Forest, and Support Vector Machine. Results report detection and classification performance with metrics such as precision, recall, accuracy, confusion matrices, and hyperparameter tuning, supported by dataset-specific experiments on CIC-Evasive-PDFMal2022 and CIC-MalMem-2022.","Machine Learning for malware detection and classification  \nMaster Thesis submitted to the Faculty of the Escola T`ecnica d’Enginyeria de Telecomunicaci´o de Barcelona Universitat Polit`ecnica de Catalunya by  \nLourdes Bruna Moralejo  \nIn partial fulfillment of the requirements for the master in Cybersecurity  \nAdvisor: Olga Le´on Abarca  \nBarcelona, 26/06/2023  \nContents  \nList of Figures 3  \nList of Tables 5  \n1 Introduction 8  \n1.1 Gantt Diagram ................................. 9  \n2 State of the art 10  \n2.1 Naive Bayes ................................... 11  \n2.2 K-Nearest Neighbours ............................. 12  \n2.3 Decision Tree .................................. 14  \n2.4 Random Forest ................................. 15  \n2.5 Support Vector Machine ............................ 16  \n3 Methodology 18  \n4 Results 22  \n4.1 Malware detection with CIC-Evasive-PDFMal2022 dataset ......... 22  \n4.1.1 Naive Bayes ............................... 22  \n4.1.2 K-Nearest Neighbours ......................... 24  \n4.1.3 Decision Tree .............................. 30  \n4.1.4 Random Forest ............................. 33  \n4.1.5 Support Vector Machine ........................ 34  \n4.1.6 Summary ................................ 36  \n4.2 Malware detection with CIC-MalMem-2022 dataset ............. 37  \n4.2.1 Bernoulli Naive Bayes ......................... 37  \n4.2.2 K-Nearest Neighbours (Hamming Distance) ............. 37  \n4.2.3 Decision Tree .............................. 38  \n4.2.4 Random Forest ............................. 39  \n4.2.5 Support Vector Machine ........................ 40  \n4.2.6 Summary ................................ 40  \n4.3 Malware classification with CIC-MalMem-2022 dataset ........... 41  \n4.3.1 Naive Bayes ............................... 41  \n4.3.2 K-Nearest Neighbours ......................... 44  \n4.3.3 Decision Tree .............................. 45  \n4.3.4 Random Forest ............................. 48  \n4.3.5 Support Vector Machine ........................ 49  \n4.3.6 Summary ................................ 49  \n5 Conclusions and future development 50  \nGlossary 52  \nReferences 53  \nList of Figures  \n1 Project’s Gantt diagram ............................ 9  \n2 Artificial Intelligence scheme 1 .......................... 10  \n3 Traditional Programming vs Machine learning2 ................ 10  \n4 Machine learning classification scheme3 ..................... 11  \n5 K-Nearest Neighbour example.......................... 12  \n6 Example of Decision Tree scheme for animal classification4 .......... 14  \n7 Visualization of a Random Forest Model making a Prediction........ 15  \n8 Conceptual overview of Support Vector Machine............... 16  \n9 Conceptual overview of Radial Basis Function5 ................ 16  \n10 Example of how random state works6 ...................... 20  \n11 The Confusion Matrix.............................. 20  \n12 Averages and variances of precision, recall and accuracy (in relation with the test size) using a Gaussian Naive Bayes algorithm (CIC-Evasive-PDFMal2022  \ndataset) ...................................... 23  \n13 Averages and variances of precision, recall and accuracy (as a function of the test size) using a Bernoulli Naive Bayes algorithm (CIC-EvasivePDFMal2022 dataset) .............................. 23  \n14 Precision, recall and accuracy calculated for different K values and test size = 0.15 (left), 0.55 (right), using the Euclidean distance (CIC-EvasivePDFMal2022 dataset) .............................. 24  \n15 Accuracy average calculated for lower test sizes and Ks, using the Euclidean distance (CIC-Evasive-PDFMal2022 dataset) ................. 25  \n16 KNN (Euclidean distance) results for 1 \u003C random state \u003C 100, 1% \u003C test size \u003C 50% and K = 3 (CIC-Evasive-PDFMal2022 dataset) ......... 25  \n17 Accuracy average calculated for lower test sizes and Ks, using the Manhattan distance (CIC-Evasive-PDFMal2022 dataset) ............... 26  \n18 KNN (Manhattan distance) results for 1 \u003C ra","cbCaivLp6pSLqTLG","https://ap.wps.com/l/cbCaivLp6pSLqTLG","pdf",3601045,1,56,"English","en",105,"# Contents\n## List of Figures\n## List of Tables\n# 1 Introduction\n## 1.1 Gantt Diagram\n# 2 State of the art\n## 2.1 Naive Bayes\n## 2.2 K-Nearest Neighbours\n## 2.3 Decision Tree\n## 2.4 Random Forest\n## 2.5 Support Vector Machine\n# 3 Methodology\n# 4 Results\n## 4.1 Malware detection with CIC-Evasive-PDFMal2022 dataset\n## 4.2 Malware detection with CIC-MalMem-2022 dataset\n## 4.3 Malware classification with CIC-MalMem-2022 dataset\n# 5 Conclusions and future development\n# Glossary\n# References","[{\"question\":\"Which machine learning algorithms are evaluated for malware detection and classification?\",\"answer\":\"The thesis evaluates Naive Bayes, K-Nearest Neighbours, Decision Tree, Random Forest, and Support Vector Machine across malware detection and classification tasks.\"},{\"question\":\"What datasets are used for the experiments?\",\"answer\":\"Experiments use the CIC-Evasive-PDFMal2022 dataset for detection and the CIC-MalMem-2022 dataset for both detection and classification.\"},{\"question\":\"How are model performance and results reported?\",\"answer\":\"Performance is reported using metrics including precision, recall, and accuracy, with visual support such as confusion matrices and evaluations over varying test sizes and hyperparameters.\"}]","Machine Learning for malware detection and classification - Master thesis | 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machine learning algorithms are evaluated for malware detection and classification?","Question",{"text":75,"@type":76},"The thesis evaluates Naive Bayes, K-Nearest Neighbours, Decision Tree, Random Forest, and Support Vector Machine across malware detection and classification tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets are used for the experiments?",{"text":80,"@type":76},"Experiments use the CIC-Evasive-PDFMal2022 dataset for detection and the CIC-MalMem-2022 dataset for both detection and classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How are model performance and results reported?",{"text":84,"@type":76},"Performance is reported using metrics including precision, recall, and accuracy, with visual support such as confusion matrices and evaluations over varying test sizes and 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