[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123933-en":3,"doc-seo-123933-105":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},123933,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Brute Force Attacks - Evaluating Machine Learning Solutions for Network Intrusion Detection","Escalating cyber threats increase the need for reliable intrusion detection systems that continuously monitor networks and identify security breaches. Traditional approaches relying on deep packet inspection and stateful protocol analysis are computationally expensive and resource intensive. This study assesses machine learning methods for network flow-based intrusion detection, with an emphasis on brute force attacks. Supervised and unsupervised models are developed and evaluated, showing that LightGBM and Decision Tree achieve the highest F1-scores among supervised methods, while an autoencoder performs best among unsupervised approaches. Results support the effectiveness of machine learning for intrusion detection.","Master Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \nBrute Force Attacks: Evaluating Machine Learning Solutions for  \nNetwork Intrusion Detection  \nBeatriz Amaro dos Santos Neto  \nMaster Thesis  \npresented as partial requirement for obtaining a Master’s Degree in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nBrute Force Attacks: Evaluating Machine Learning Solutions for Network Intrusion  \nDetection  \nby  \nBeatriz Amaro dos Santos Neto  \nMaster Thesis presented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics, with a specialization in Business Analytics  \nSupervised by  \nRoberto Henriques, PhD, NOVA Information Management School  \nNovember, 2023  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nFrankfurt am Main, 27/11/2023  \nACKNOWLEDGEMENTS  \nI am sincerely thankful to my supervisor, Roberto Henriques, for his guidance and support throughout the development of this work. I extend my heartfelt gratitude to my amazing family and friends for their endless encouragement and understanding.  \nABSTRACT  \nFacing the escalating frequency and sophistication of cyber threats in the modern digital era, intrusion detection systems are essential in protecting networks and computer systems. These systems monitor the network and detect potential security breaches. Traditional intrusion detection systems employ computationally expensive deep packet inspection and resource-intensive stateful protocol analysis to identify network threats. To overcome these challenges, researchers are progressively exploring network flow-based intrusion detection as an effective alternative. This study evaluates machine learning solutions for network flowbased intrusion detection, focusing mainly on brute force attacks. Both supervised and unsupervised learning methods are employed, and the results are comprehensively analysed and discussed. LightGBM and Decision Tree, with a F1-score of 99.99% and 99.98%, respectively, proved to be the best-performing models among the supervised methods. In contrast, with a F1-score of 96. 10%, the autoencoder outperformed the unsupervised methods. The findings of this study validate the effectiveness of machine learning algorithms for network intrusion detection systems.  \nKEYWORDS  \nCybersecurity; Intrusion Detection System; Machine Learning; Classification; Brute Force  \nSustainable Development Goals (SDG):  \nTABLE OF CONTENTS  \n1. Introduction .................................................................................................................. 1  \n2. Literature review ..........................................................................................................4  \n3. Methodology ................................................................................................................9  \n3.1. Data Understanding ............................................................................................ 10  \n3.2. Data Preparation ................................................................................................. 11  \n3.2.1. Data Cleaning................................................................................................ 11  \n3.2.2.Target transformation and dataset undersampling.....................................12  \n3.2.3. Feature Selection.......................................................................................... 12  \n3","cbCaipSnA1lyKJRm","https://ap.wps.com/l/cbCaipSnA1lyKJRm","pdf",788296,1,46,"English","en",105,"# 1. Introduction\n# 2. Literature review\n# 3. Methodology\n## 3.1. Data Understanding\n## 3.2. Data Preparation\n## 3.3. Modelling\n## 3.4. Evaluation\n# 4. Results and discussion\n# 5. Conclusions and future works","[{\"question\":\"What intrusion detection approach is evaluated in the study?\",\"answer\":\"The study evaluates machine learning solutions for network flow-based intrusion detection, focusing on brute force attacks rather than traditional deep packet inspection and stateful protocol analysis.\"},{\"question\":\"Which models performed best among the supervised learning methods?\",\"answer\":\"LightGBM and Decision Tree achieved the best results among supervised models, with F1-scores around 99.99% and 99.98% respectively.\"},{\"question\":\"How did the unsupervised methods compare, and which one was best?\",\"answer\":\"Among unsupervised methods, the autoencoder outperformed the others, reaching an F1-score around 96.10%.\"}]","Brute Force Attacks - Evaluating Machine Learning Solutions for Network Intrusion Detection | PDF",1785819319,116,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"brute-force-attacks-evaluating-machine-learning-solutions-for-network-intrusion-detection","",{"@graph":36,"@context":85},[37,54,68],{"@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/brute-force-attacks-evaluating-machine-learning-solutions-for-network-intrusion-detection/123933/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What intrusion detection approach is evaluated in the study?","Question",{"text":75,"@type":76},"The study evaluates machine learning solutions for network flow-based intrusion detection, focusing on brute force attacks rather than traditional deep packet inspection and stateful protocol analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models performed best among the supervised learning methods?",{"text":80,"@type":76},"LightGBM and Decision Tree achieved the best results among supervised models, with F1-scores around 99.99% and 99.98% respectively.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the unsupervised methods compare, and which one was best?",{"text":84,"@type":76},"Among unsupervised methods, the autoencoder outperformed the others, reaching an F1-score around 96.10%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]