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The manual specifies system hardware, Windows 11 environment, Python 3.8.10, and required libraries, then outlines functional and non-functional requirements including real-time detection, scalability, robustness, and modular maintenance. It details data collection with two large datasets, preprocessing for missing values and payload conversion, SMOTE-based balancing, feature engineering and selection, model development using Random Forest and a Voting Classifier, evaluation metrics, and a step-by-step implementation pipeline. Reported Random Forest performance reaches 89.61% accuracy, 91.24% precision, and 87.64% recall.",{"@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":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/configuration-manual-practicum-part-2-enhancing-vehicle-security-intrusion-detection-using-machine-learning/290866/",{"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/configuration-manual-practicum-part-2-enhancing-vehicle-security-intrusion-detection-using-machine-learning/290866.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the purpose and scope of the intrusion detection system described in the manual?","Question",{"text":113,"@type":114},"The manual defines a machine-learning-based framework for intrusion detection, including its purpose and scope while addressing high false positives, real-time processing, and scalability across vehicle models.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which hardware and software components are required?",{"text":118,"@type":114},"It specifies an Intel Core i7 processor, at least 8GB RAM and 512GB storage, Windows 11, Python 3.8.10, and libraries such as pandas, numpy, scikit-learn, matplotlib, and imbalanced-learn using Jupyter Notebook.",{"name":120,"@type":111,"acceptedAnswer":121},"How is the dataset handled before training the models?",{"text":122,"@type":114},"The manual covers dataset import, missing value removal using a default placeholder payload, conversion of hexadecimal payloads to numeric lists, removal of insignificant columns, and class balancing using SMOTE.","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},290866,1789642844,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"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":44,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Configuration Manual  \nPracticum Part 2  \nMsc Cyber Security  \nAnusha Varghese Student ID: 23217693  \nSchool of Computing National College of Ireland  \nSupervisor: Mr. Vikas Sahni  \nNational College of Ireland MSc Project Submission Sheet  \nSchool of Computing  \nStudent Name: Anusha Varghese  \nStudent ID: 23217693  \nProgramme: Msc Cyber Security Year: 2024-2025  \nModule: Practicum Part 2  \nLecturer: Mr. Vikas Sahni  \nSubmission Due  \nDate: 12 December 2024  \nProject Title: Enhancing Vehicle Security: Intrusion Detection Using Machine Learning  \nWord Count: 744 Page Count: 7  \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 .  \nSignature: Anusha Varghese  \nDate: 12 December 2024  \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): |  |\n\nConfiguration Manual  \nAnusha Varghese  \nStudent ID: 23217693  \n1 Introduction  \nThis manual defines the framework for the intrusion detection system based on machine learning. It describes its purpose and scope, including high false positives, real-time processing, and scalability across different vehicle models.  \n2 Hardware Specifications  \n• Processor: Intel Core i7 .  \n• Memory (RAM): 8GB minimum.  \n• Storage: 512 GB minimum.  \n• Peripherals: Input/output devices for monitoring and testing.  \n3 Software Specifications  \n• Operating System: Windows 11.  \n• Programming Language: Python 3.8.10.  \n• Libraries: pandas, numpy, scikit-learn, matplotlib, imbalanced-learn.  \n• Development Environment: Jupyter Notebook.  \n4 System Requirements  \n4.1 Functional Requirements  \n• Support for heterogeneous automotive datasets.  \n• Ability to handle imbalanced datasets using advanced resampling techniques.  \n• Feature engineering to enhance interpretability and accuracy.  \n• Deployment of machine learning algorithms with high classification performance.  \n• Output detailed metrics like confusion matrices and evaluation scores.  \n4.2 Non-Functional Requirements  \n• Real-time detection capability.  \n• Scalability for large datasets.  \n• Robustness to noisy or incomplete inputs.  \n• Modular design for ease of maintenance.  \n5 Data Preparation  \n5.1 Data Collection  \nFigure 1: Importing Libraries and Dataset  \n• Two datasets: attack-free and DoS-attack datasets with over 2.8 million combined entries.  \n• Key features:  \n• Timestamp: Floating-point representation of the time.  \n• Identifier: Unique network message identifiers.  \n• RTR and DLC: Indicators of data transmission characteristics.  \n• Data: Hexadecimal strings encapsulating payloads.  \n5.2 Preprocessing  \nFigure 2: Missing Value Removal  \n• Missing values replaced with a default placeholder payload (['00'] * 8) .  \n• Payload data transformed from hexadecimal to numerical lists.  \n• Removed insignificant columns like RTR to reduce the computational burden.  \n5.3 Data Balancing  \nFigure 3: Data Balanci","cbCaim0DMr3RGiO9","https://ap.wps.com/l/cbCaim0DMr3RGiO9","pdf",314642,"English","# 1 Introduction\n# 2 Hardware Specifications\n# 3 Software Specifications\n# 4 System Requirements\n## 4.1 Functional Requirements\n## 4.2 Non-Functional Requirements\n# 5 Data Preparation\n## 5.1 Data Collection\n## 5.2 Preprocessing\n## 5.3 Data Balancing\n# 6 Feature Engineering\n## 6.1 Extracted Features\n## 6.2 Feature Selection\n# 7 Model Development\n## 7.1 Machine Learning Models\n## 7.2 Training Workflow\n# 8 Evaluation Metrics\n# 9 Implementation Pipeline\n## 9.1 Steps\n## 9.2 Code Modularization Functions\n# 10 Results and Discussion\n## 10.1 Random Forest Classifier","[{\"question\":\"What is the purpose and scope of the intrusion detection system described in the manual?\",\"answer\":\"The manual defines a machine-learning-based framework for intrusion detection, including its purpose and scope while addressing high false positives, real-time processing, and scalability across vehicle models.\"},{\"question\":\"Which hardware and software components are required?\",\"answer\":\"It specifies an Intel Core i7 processor, at least 8GB RAM and 512GB storage, Windows 11, Python 3.8.10, and libraries such as pandas, numpy, scikit-learn, matplotlib, and imbalanced-learn using Jupyter Notebook.\"},{\"question\":\"How is the dataset handled before training the models?\",\"answer\":\"The manual covers dataset import, missing value removal using a default placeholder payload, conversion of hexadecimal payloads to numeric lists, removal of insignificant columns, and class balancing using SMOTE.\"}]","Configuration Manual - Practicum Part 2 - Enhancing Vehicle Security: Intrusion Detection Using Machine Learning | PDF",23]