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The project evaluates model training approaches including CNN, RNN, and reinforcement learning to predict threats based on network traffic patterns. Hardware and software requirements are specified for high-performance macOS and Windows configurations, alongside Python 3.8+, Anaconda, Jupyter Notebook, and core ML libraries such as TensorFlow, Keras, scikit-learn, pandas, and NumPy. Code demonstration steps cover deployment workflow using SageMaker, S3 model saving, and local application execution.",{"@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":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/assessing-machine-learning-algorithms-for-cloud-computing-threat-detection/290527/",{"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/assessing-machine-learning-algorithms-for-cloud-computing-threat-detection/290527.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-21","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What machine learning methods are used for threat prediction in the project?","Question",{"text":112,"@type":113},"The study uses machine learning for threat detection, including CNN, RNN, and reinforcement learning models for predicting threats.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which dataset is used to train and evaluate the models?",{"text":117,"@type":113},"The project uses the CICIDS2017 dataset from the Canadian Institute for Cybersecurity, intended for network intrusion detection system evaluation.",{"name":119,"@type":110,"acceptedAnswer":120},"What are the key system software requirements for running the project?",{"text":121,"@type":113},"Requirements include Windows 11 Pro or macOS Ventura or later, Python 3.8+ with Anaconda and Jupyter Notebook, and ML libraries such as TensorFlow, Keras, scikit-learn, pandas, NumPy, and Matplotlib.","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},290527,1789641942,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"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},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Configuration Manual  \nMSc Research Project MSc in Cloud Computing  \nZaid Ali Khan Student ID: x22204016  \nSchool of Computing National College of Ireland  \nSupervisor: Shreyas Setlur Arun  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name:  \nStudent ID:  \nZaid Ali Khan  \n22204016  \nProgramme: MSc in Cloud Computing  \n  Year: 2023-2024  \nModule: Research in Computing  \nLecturer: Shreyas Setlur Arun  \nSubmission Due  \nDate: 16, September 2024  \nProject Title:  \nAssessing Machine learning Algorithms for Cloud Computing Threat Detection  \n………………………………………………………………………………………………………….………  \nWord Count: 413 4  \n  Page Count:   .  \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.  \nZaid Ali Khan  \nSignature: ………………………………………………………………………………………………………………  \n16, September 2024  \nDate: …………………………………………………………………………………………………………..  \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 for your 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):\u003Cbr>|  |\n\nConfiguration Manual  \nZaid Ali Khan  \nStudent ID: 22204016  \n1. Introduction  \nThe study demonstrates the utilization of machine learning for threat detection. The study considers the usage of CICIDS 2017 Dataset for training CNN, RNN and Reinforcement learning model for prediction of threats.  \n2. Hardware Requirements  \n2.1 For macOS (High-Performance Mac Configuration)  \n• Model: Mac Pro or MacBook Pro  \n• Processor: Apple M1 Max or Intel Core i7/i9 processor  \n• Memory: 32 GB or 64 GB of RAM (depending on workload intensity)  \n• Storage: 2 TB SSD  \n• Graphics: Integrated Apple GPU or AMD Radeon Pro (for Mac Pro)  \n• Operating System: macOS Ventura or later  \n• Network: High-speed Ethernet or Wi-Fi 6 for data-intensive tasks  \n2.2 For Windows(High-Performance Windows Configuration)  \n• Model: Workstation PC (e.g., Dell Precision, HP Z Workstation) or high-end laptop (e.g., Dell)  \n• Processor: Intel Core i7/i9 or AMD Ryzen 9  \n• Memory: 32 GB or 64 GB of RAM  \n• Storage: 2 TB SSD  \n• Graphics: NVIDIA RTX 3060 or higher for workstation: Integrated Intel Iris Xe for laptops  \n• Operating System: Windows 11 Pro  \n• Network: Gigabit Ethernet or Wi-Fi 6  \n3. Software Requirements  \n3.1 Operating System:  \n• Windows 11 Pro or macOS Ventura or later  \n3.2 Programming Environment:  \n• Python: Version 3.8 or later  \n• Anaconda: For managing Python packages and environments  \n• Jupyter Notebook: For interactive development and documentation  \n3.3 Machine Learning Libraries:  \n• TensorFlow: For deep learning model development  \n• Keras: High-level neural networks API for TensorFlow  \n• Scikit-learn: For traditional machine learning algorithms  \n• Pandas: For data manipulation and analysis  \n• NumPy: For numerical computations  \n• Matplotlib: For plotting and visualization  \n3.4 Integrated Development Environment (IDE):  \n• Visual Studio Code: Lightweight editor with Python sup","cbCaihN29LE46ZDP","https://ap.wps.com/l/cbCaihN29LE46ZDP","pdf",469174,"English","# Introduction\n# Hardware Requirements\n## For macOS (High-Performance Mac Configuration)\n## For Windows (High-Performance Windows Configuration)\n# Software Requirements\n## Operating System\n## Programming Environment\n## Machine Learning Libraries\n## IDE\n# Dataset Used\n# Follow below steps for the code demonstration","[{\"question\":\"What machine learning methods are used for threat prediction in the project?\",\"answer\":\"The study uses machine learning for threat detection, including CNN, RNN, and reinforcement learning models for predicting threats.\"},{\"question\":\"Which dataset is used to train and evaluate the models?\",\"answer\":\"The project uses the CICIDS2017 dataset from the Canadian Institute for Cybersecurity, intended for network intrusion detection system evaluation.\"},{\"question\":\"What are the key system software requirements for running the project?\",\"answer\":\"Requirements include Windows 11 Pro or macOS Ventura or later, Python 3.8+ with Anaconda and Jupyter Notebook, and ML libraries such as TensorFlow, Keras, scikit-learn, pandas, NumPy, and Matplotlib.\"}]","Assessing Machine learning Algorithms for Cloud Computing Threat Detection | PDF",15]