[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123332-en":3,"doc-seo-123332-105":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},123332,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Intrusion Detection Classification Using Machine Learning - Thesis Signature Page and Table of Contents","Master of Science thesis on intrusion detection classification using machine learning, focused on malicious code injection and the taxonomy of DDoS attack types and characteristics. The work reviews related literature, describes the DDoS evolution dataset, and specifies engineered features used for modeling. It outlines a complete data preparation workflow including column cleaning, datatype conversion, dataset splitting, and feature scaling. Performance evaluation is presented with hardware and environment settings, metric evolution analysis, and model comparison across Random Forest, SVM, Decision Tree, XGB, Naïve Bayes, MLP, and LSTM.","CALIFORNIA STATE UNIVERSITY SAN MARCOS  \nTHESIS SIGNATURE PAGE  \nTHESIS SUBMITTED IN PARTIAL FULFILLMENT  \nOF THE REQUIREMENTS FOR THE DEGREE  \nMASTER OF SCIENCE  \nIN  \nCOMPUTER SCIENCE  \nTHESIS TITLE: INTRUSION DETECTION CLASSIFICATION USING MACHINE LEARNING  \nAUTHOR: ARATEE GIRDHARBHAI MISTRY  \nDATE OF SUCCESSFUL DEFENSE: 05/03/2023  \nTHE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE.  \nDr. Nahid Ebrahimi Majd  \nTHESIS COMMITTEE CHAIR  \nDr. Sreedevi Gutta  \nTHESIS COMMITTEE MEMBER  \nSIGNATURE  \nDATE  \nACKNOWLEDGMENT  \nI would like to express my sincere gratitude to my esteemed thesis advisor, Dr. Nahid Ebrahimi Majd, for her invaluable guidance, support, and mentorship throughout the course of my research. Her profound knowledge, insightful feedback, and astute observations have been instrumental in shaping my research and driving me to push the boundaries of my inquiry. I am equally indebted to the member of my thesis committee, Dr. Sreedevi Gutta for her thoughtful critiques and constructive suggestions, which have significantly enhanced the rigor and scholarly merit of my work. I would like to extend my profound appreciation to the Department of Computer Science at California State University San Marcos, for providing me with a stimulating research environment and world-class resources that have been essential in enabling me to pursue this research to its fullest potential. I am deeply grateful to my parents, for their unwavering love, support, and encouragement, which have sustained me throughout my academic journey and beyond. Finally, I would like to extend my heartfelt thanks to all my colleagues, friends, and acquaintances who have provided me with their support, encouragement, and inspiration during this journey. Your unwavering commitment to academic excellence and scholarly pursuits has been a source of inspiration and motivation to me, and for that, I am deeply grateful. Once again, thank you all for your support, and for making this accomplishment possible.  \nTable of Contents  \nI. INTRODUCTION......................................................................................................................................................................4  \nA.) Malicious code injection .....................................................................................................................................................5  \nII. LITERATURE REVIEW .........................................................................................................................................................6  \nIII. CLASSIFICATION OF DDOS ATTACKS: UNDERSTANDING THE TYPES AND CHARACTERISTICS ...................7  \nIV. METHODOLOGY ..................................................................................................................................................................8  \nA.Description of DDoS Evolution Dataset ................................................................................................................................9  \nB.Features Used in this Paper.................................................................................................................................................. 10  \nV. DATA PREPROCESSING .................................................................................................................................................... 11  \nA.Removing Irrelevant and non-unique Columns ...................................................................................................................11  \nB. Numerical Datatype Conversion for Categorial Columns ..................................................................................................11  \nC. Data splitting.......................................................................................................................................................................","cbCaidOc53ZN8lmL","https://ap.wps.com/l/cbCaidOc53ZN8lmL","pdf",1046590,1,30,"English","en",105,"# INTRODUCTION\n## Malicious code injection\n# LITERATURE REVIEW\n# CLASSIFICATION OF DDOS ATTACKS: UNDERSTANDING THE TYPES AND CHARACTERISTICS\n# METHODOLOGY\n## Description of DDoS Evolution Dataset\n## Features Used in this Paper\n# DATA PREPROCESSING\n## Removing Irrelevant and non-unique Columns\n## Numerical Datatype Conversion for Categorial Columns\n## Data splitting\n## Feature Scaling\n# PERFORMANCE EVALUATION AND ANALYSIS\n## Hardware and Environment Setting\n## Evolution of Metrics\n# MACHINE LEARNING MODELS\n## Random Forest Classifier (RF)\n## Support Vector Machine (SVM)\n## Decision tree classifier (DT)\n## XGB Classifier\n## Naïve Bayes Classifier (NB)\n## Multi Layer Perceptron (MLP)\n## Long Short Term Memory (LSTM)","[{\"question\":\"What is the main topic of the thesis?\",\"answer\":\"The thesis focuses on intrusion detection classification using machine learning, including malicious code injection and classifying DDoS attack types.\"},{\"question\":\"How does the methodology prepare data for modeling?\",\"answer\":\"It removes irrelevant and non-unique columns, converts datatypes for categorical columns, splits the dataset, and applies feature scaling.\"},{\"question\":\"Which machine learning models are evaluated in the performance section?\",\"answer\":\"The thesis evaluates Random Forest, Support Vector Machine, Decision Tree, XGB, Naïve Bayes, Multi Layer Perceptron, and Long Short Term Memory, with performance metrics tracked over evaluation.\"}]","Intrusion Detection Classification Using Machine Learning - 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