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The manual specifies recommended hardware and required software (Jupyter Notebook and Anaconda with Python libraries), describes the Kaggle dataset split into training and testing CSV files, and outlines a Jupyter-based workflow. It covers data pre-processing and feature engineering steps, imports and code explanations, and initial exploratory analysis and visualization approaches for network intrusion classification.",{"@graph":69,"@context":123},[70,84,106],{"@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":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/harnessing-evolving-machine-learning-techniques-for-enhanced-intrusion-detection-system/290871/",{"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/harnessing-evolving-machine-learning-techniques-for-enhanced-intrusion-detection-system/290871.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-21","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the goal of the intrusion detection project described in the manual?","Question",{"text":113,"@type":114},"The project aims to improve intrusion detection capabilities by using advanced machine learning techniques, helping identify intrusions and classify them into normal and distinct groups.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What hardware and software are recommended for running the Jupyter Notebook workflow?",{"text":118,"@type":114},"The manual recommends an Intel Core i5 or higher, at least 8 GB RAM, and sufficient storage, with an optional NVIDIA GTX 1050 Ti or better GPU. It also requires Jupyter Notebook (6.0+) and Anaconda (2020.11+), along with common Python libraries such as pandas, numpy, matplotlib, seaborn, scikit-learn components, and tensorflow.",{"name":120,"@type":111,"acceptedAnswer":121},"Where does the dataset come from and how is it organized for training and testing?",{"text":122,"@type":114},"The dataset is downloaded from Kaggle and stored in the project folder under an IDS_dataset directory. It is divided into training and testing files: /Train_data.csv and /Test_data.csv, imported within the Jupyter environment.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290871,1789642851,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","| Configuration Manual | |\n| --- | --- |\n| MSc Research Project MSc Cyber-security\u003Cbr>Viral Sonavadia Student ID: X23103116\u003Cbr>School of Computing National College of Ireland\u003Cbr>Supervisor: Niall Heffernan |  |\n\nNational College of Ireland  \nProject Submission Sheet  \nStudent Name: Viral Sonavadia  \nStudent ID: X23103116  \nProgramme: MSc in Cybersecurity Year: 2023-2024  \nModule: MSc Research Project  \nLecturer: Mr. Niall Heffernan  \nSubmission Due 12th August 2024 Date:  \nProject Title: HARNESSING EVOLVING MACHINE LEARNING TECHNIQUES FOR  \nENHANCED INTRUSION DETECTION SYSTEM  \nWord Count: 918 Page Count: 12  \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 references section. Students are encouraged to use the Harvard Referencing Standard supplied by the Library. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action. Students may be required to undergo a viva (oral examination) if there is suspicion about the validity of their submitted work.  \nSignature: Viral Sonavadia  \nDate: 12th August 2024  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \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,\u003Cbr>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\nConfiguration Manual  \n1. Introduction  \nThe project \"Harnessing Evolving Machine Learning Techniques for Enhanced Intrusion Detection System\" aims to improve Intrusion Detection Systems (IDS) capabilities using advanced machine learning techniques This document describes the necessary hardware and software configuration, dataset description and deployment steps using Jupyter Notebook.  \n2. Overview of the program  \nThis project involves the analysis and classification of network access data using various machine learning models. The workflow includes data pre-processing, feature engineering, training multiple machine learning models, and testing their performance on test data. The goal is to identify intrusions and correctly classify them into normal and discrete groups.  \n3. Hardware/software requirements  \n3.1. Hardware production  \nThe following hardware settings are recommended for smooth operation.  \n• Processor: Intel Core i5 or higher (using i5)  \n• RAM: 8 GB or more (16GB)  \n• Storage: 100 GB free space (400GB free)  \n• GPU (optional, for fast model training): NVIDIA GTX 1050 Ti or better (Intel® Iris® Xe Graphics)  \n3.2. Software  \nThe following software is required for the project:  \n• Jupyter Notebook: Used to run and write code.  \n• Anaconda: To manage the Python environment and dependencies.  \nVersion Requirements:  \n• Jupyter Notebook: Version 6.0 or later  \n• Anaconda: Version 2020.11 or later  \nEnsure that the necessary Python libraries are installed. These include panda, numpy, mutplotlib, seaborn, scikit-sua, and tensorflow.  \n4. The data set  \nThe dataset used for this project came from Kaggle. The data are divided into training and testing phases:  \nAs they were imported on the online Jupyter  \n• Training information: /Train_data.csv  \n• Data Test: /Test_data.csv  \n\n| The dataset is downloaded from Kaggle and add it to the IDS_dataset directory in project folder.\u003Cbr>5","cbCaihJUcEGtXcqV","https://ap.wps.com/l/cbCaihJUcEGtXcqV","pdf",839457,12,"English","# 1. Introduction\n# 2. Overview of the program\n# 3. Hardware/software requirements\n## 3.1. Hardware production\n## 3.2. Software\n# 4. The data set\n# 5. Implementation of Project in Jupyter\n## 5.1. Import Libraries\n## 5.2. Load and Inspect Data\n## 5.3. Data Overview\n## 5.4. Data Visualization","[{\"question\":\"What is the goal of the intrusion detection project described in the manual?\",\"answer\":\"The project aims to improve intrusion detection capabilities by using advanced machine learning techniques, helping identify intrusions and classify them into normal and distinct groups.\"},{\"question\":\"What hardware and software are recommended for running the Jupyter Notebook workflow?\",\"answer\":\"The manual recommends an Intel Core i5 or higher, at least 8 GB RAM, and sufficient storage, with an optional NVIDIA GTX 1050 Ti or better GPU. It also requires Jupyter Notebook (6.0+) and Anaconda (2020.11+), along with common Python libraries such as pandas, numpy, matplotlib, seaborn, scikit-learn components, and tensorflow.\"},{\"question\":\"Where does the dataset come from and how is it organized for training and testing?\",\"answer\":\"The dataset is downloaded from Kaggle and stored in the project folder under an IDS_dataset directory. It is divided into training and testing files: /Train_data.csv and /Test_data.csv, imported within the Jupyter environment.\"}]","HARNESSING EVOLVING MACHINE LEARNING TECHNIQUES FOR ENHANCED INTRUSION DETECTION SYSTEM | PDF"]