[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290857-105":59,"doc-detail-290857-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","cybersecurity-fortification-through-machine-learning-predictive-models-for-malware-detection-in-network-environments-msc-research-project","Cybersecurity Fortification through Machine Learning - Predictive Models for Malware Detection in Network Environments - MSc Research Project","","With an emphasis on virus detection in network contexts, this MSc research project examines cybersecurity hardening through predictive machine learning models. Using multimodal learning with RNN, ANN, and CNN, it builds resilient approaches to detect harmful activity. Training and evaluation rely on established datasets, including Microsoft malware prediction resources, to cover representative malicious patterns. The study applies downsampling, explores CNN behavior alongside conventional models, and uses feature engineering and encoding to improve spatial understanding for more precise identification. Results indicate downsampling to 100,000 rows manages resources while raising generalization questions, and CNNs show promising spatial dependency capture benefits.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/cybersecurity-fortification-through-machine-learning-predictive-models-for-malware-detection-in-network-environments-msc-research-project/290857/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/cybersecurity-fortification-through-machine-learning-predictive-models-for-malware-detection-in-network-environments-msc-research-project/290857.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the primary research goal of this project?","Question",{"text":112,"@type":113},"The project aims to strengthen cybersecurity by using predictive models to detect malware in network environments, with a focus on virus detection in network contexts.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning models are used for malware detection?",{"text":117,"@type":113},"The research applies multimodal machine learning techniques, including RNN, ANN, and CNN, to build resilient malware detection models.",{"name":119,"@type":110,"acceptedAnswer":120},"How does downsampling affect the study and its findings?",{"text":121,"@type":113},"The study uses downsampling to reduce the dataset to 100,000 rows, which helps manage computer resources but leads to questions about generalisation to larger datasets.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290857,1789642831,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":41,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","”Cybersecurity Fortification through Machine Learning: Predictive Models for Malware Detection in Network Environments”  \nMSc Research Project Masters in Artificial Intelligence  \nHudson Paul Rajesh  \nStudent ID: x22181920  \nSchool of Computing National College of Ireland  \nSupervisor: Muslim Jameel Syed  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Hudson Paul Rajesh |\n| --- | --- |\n| Student ID: | x22181920 |\n| Programme: | Masters in Artificial Intelligence |\n| Year: | 2023 |\n| Module: | MSc Research Project |\n| Supervisor: | Muslim Jameel Syed |\n| Submission Due Date: | 14/12/2023 |\n| Project Title: | ”Cybersecurity Fortification through Machine Learning: Predictive Models for Malware Detection in Network Environments” |\n| Word Count: | 9250 |\n| Page Count: | 28 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | Hudson Paul Rajesh |\n| --- | --- |\n| Date: | 14/12/2023 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\n”Cybersecurity Fortification through Machine Learning: Predictive Models for Malware Detection in  \nNetwork Environments”  \nHudson Paul Rajesh  \nx22181920  \nAbstract  \nWith a particular emphasis on virus detection in network contexts, this research project explores the field of cybersecurity. By using a multimodal approach, we apply well-known machine learning techniques, such as Recurrent Neural Networks (RNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), to build resilient models for the detection of harmful activity. The research employs well-known datasets for training and assessing the models’ effectiveness, such as those from the Microsoft malware prediction repository. To ensure the effectiveness of our models, we leverage established datasets, including data from the Microsoft Malware Prediction Database. These datasets serve as a valuable resource for training and evaluating the performance of machine learning models and provide a variety of representative malicious patterns for comprehensive analysis. Our research on the application of RNN, ANN and CNN in malware detection aims to improve the accuracy and effectiveness of cyber security measures. By leveraging the power of these machine learning structures, we aim to strengthen network security, creating proactive defences against cyber threats.  \nThe goal of this research project is to strengthen cybersecurity by employing predictive models to detect malware in network environments. The study carefully uses downsampling methods and investigates how well Convolutional Neural Networks (CNNs) operate in conjunction with conventional machine learning models. The main objective of the three investigations, which involve extensive feature engineering and encoding methodologies, is to improve spatial ","cbCaidwtBljuTmmW","https://ap.wps.com/l/cbCaidwtBljuTmmW","pdf",3312799,"English","# 1 Introduction\n## 1.1 Contextualizing the Problem","[{\"question\":\"What is the primary research goal of this project?\",\"answer\":\"The project aims to strengthen cybersecurity by using predictive models to detect malware in network environments, with a focus on virus detection in network contexts.\"},{\"question\":\"Which machine learning models are used for malware detection?\",\"answer\":\"The research applies multimodal machine learning techniques, including RNN, ANN, and CNN, to build resilient malware detection models.\"},{\"question\":\"How does downsampling affect the study and its findings?\",\"answer\":\"The study uses downsampling to reduce the dataset to 100,000 rows, which helps manage computer resources but leads to questions about generalisation to larger datasets.\"}]","Cybersecurity Fortification through Machine Learning - Predictive Models for Malware Detection in Network Environments - MSc Research Project | PDF",76]