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\nStudent ID: x22226885  \nSchool of Computing National College of Ireland  \nSupervisor: Jorge Mario Cortes Mendoza  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Zeba Mahfuz |\n| --- | --- |\n| Student ID: | x22226885 |\n| Programme: | MSc in Cloud Computing |\n| Year: | 2023-2024 |\n| Module: | Research in Computing |\n| Supervisor: | Jorge Mario Cortes Mendoza |\n| Submission Due Date: | 16/09/2024 |\n| Project Title: | Machine Learning Approaches to analyze Intrusion Detection Risk under Cloud Computing |\n| Word Count: | 490 |\n| Page Count: | 5 |\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: | Zeba Mahfuz |\n| --- | --- |\n| Date: | 16-09-2024 |\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\nConfiguration Manual  \nZeba Mahfuz  \nx22226885  \n16-09-2024  \n1 Introduction  \n1.1 Purpose of the Manual  \nThis configuration manual offers in depth instructions on how to establish and customize an intrusion detection system based on machine learning within a computing setting. It features step by step guidance and visual aids to help users effectively implement and manage the system.  \n1.2 Overview of the Project  \nThe project is about using machine learning methods to examine network traffic and identify possible security risks in cloud setups. By using algorithms such as K Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) the system categorizes network actions as either normal or intrusive boosting cloud security, with risk evaluation and identification.  \n2 System Configuration  \n2.1 Hardware Requirements  \n• Processor: Minimum 2.4 GHz (Intel Core i5 or higher recommended)  \n• RAM: At least 8 GB  \n• Storage: Minimum 20 GB of free space  \n• Operating System: 64-bit (Windows, macOS, Linux)  \n2.2 Software Requirements  \n• Python: Version 3.8 or higher  \n• Jupyter Notebook: Accessible via Google Colab or installed locally  \n• Required Libraries:  \nScikit-learn: Version 0 .24.2 - for machine learning algorithms and data preprocessing.  \nPandas: Version 1 .3.3-for data manipulation and handling.  \nNumPy: Version 1 .20.3-for numerical operations and data processing.  \nMatplotlib: Version 3 .4.3-for data visualization and creating plots.  \nSeaborn: Version 0 .11.1-for enhanced statistical data visualization.  \n• Cloud Platform: Google Colaboratory or an equivalent cloud-based Jupyter notebook environment  \n3 Project Setup  \n3.1 Environment Setup  \n1. Google Colaboratory:  \n• Access Google Colaboratory at: Google Colab  \n• Create a new notebook for coding and executing the project.  \n• Import necessary libraries and datasets as shown in the code below.  \n2. Local Environment Setup:  \n• Install Anaconda for package and environment managem","cbCaijNzUBhCIire","https://ap.wps.com/l/cbCaijNzUBhCIire","pdf",1936016,"English","# 1 Introduction\n## 1.1 Purpose of the Manual\n## 1.2 Overview of the Project\n# 2 System Configuration\n## 2.1 Hardware Requirements\n## 2.2 Software Requirements\n# 3 Project Setup\n## 3.1 Environment Setup\n## 3.2 Dataset Preparation\n## 3.3 Data Pre-processing\n## 3.4 Model Training and Evaluation\n## 3.5 Performance Evaluation","[{\"question\":\"What is the purpose of this configuration manual?\",\"answer\":\"It explains how to establish and customize a machine learning-based intrusion detection system in a computing environment, with step-by-step guidance and visual aids.\"},{\"question\":\"Which machine learning models are implemented for training and evaluation?\",\"answer\":\"The project implements a Random Classifier, Multilayer Perceptron (MLP), Logistic Regression, and k-Nearest Neighbors (KNN).\"},{\"question\":\"How is performance evaluated in the project?\",\"answer\":\"Models are assessed using accuracy, precision, recall, F1-score, cross-validation, and confusion matrices, with results reported in tables and figures.\"}]","Machine Learning Approaches to analyze Intrusion Detection Risk under Cloud Computing - Configuration Manual | PDF",18]