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The project addresses class imbalance, feature importance, and hyperparameter tuning to identify an environment that improves model accuracy and efficiency. The document also supports reproducibility by specifying system setup, hardware and software requirements, dataset sources used in the study, and required Python library versions to run the accompanying notebooks consistently. 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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.  \nMuhammad Zaeem  \nSignature:    \n12th August, 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): |  |\n\nEnhancing Network Security Using Machine Learning Model-Agnostic Approach on Diverse Datasets  \nMuhammad Zaeem  \nX23108088  \n1 Introduction  \nIt is a mode-agnostic project for the in-depth analysis of different network traffic classifications and intrusion datasets to address challenges like class imbalance, feature importance, and hyperparameter tuning aiming to find the best-performing environment for model to improve their accuracy and efficiency. Another goal is to find an inference model that can be suggested to be implemented in real-life intrusion detection systems and firewalls to enhance network security. This configuration manual is designed to provide all the details that can help anyone recreate this research.  \n2 System Requirements  \nFor the efficient execution of this research artefact, the following hardware and software requirements should be considered beforehand  \n2.1 Hardware requirements  \nTable 1. shows the needed hardware requirements to run the code artefact  \n\n| Operating System (OS) | Windows 10 or similar OS |\n| --- | --- |\n| Processor | Intel i5 8th Gen or above |\n| RAM | 16 GB DDR4 |\n| Storage | 50 GB available space |\n\nTable 1: Hardware Specifications  \n2.2 Software Requirements  \nMicrosoft Excel is used for the early exploration of datasets. For the implementation of the project, the Jupyter Notebook IDE from Anaconda Environment is used which has Python kernel preinstalled in it.  \n• Python Kernel version: 3.11.4  \n• Jupyter Notebook version: 7.0.0  \n3 Dataset Collection  \nThree datasets are used in this research. The first one is the network traffic classification dataset taken from the UCI Machine Learning repository 1 named as Internet Firewall Dataset. The second Dataset is the UNSW NB15 datas","cbCaiu4nj7F0SHVH","https://ap.wps.com/l/cbCaiu4nj7F0SHVH","pdf",1358969,24,"English","# 1 Introduction\n# 2 System Requirements\n## 2.1 Hardware requirements\n## 2.2 Software Requirements\n# 3 Dataset Collection\n# 4 Project Development","[{\"question\":\"What is the main aim of the project described in this configuration manual?\",\"answer\":\"To perform an in-depth, mode-agnostic analysis of network traffic classification and intrusion datasets, improving model accuracy and efficiency while addressing class imbalance, feature importance, and hyperparameter tuning.\"},{\"question\":\"Which datasets are used in the research?\",\"answer\":\"Internet Firewall Dataset (UCI), UNSW NB15 (Kaggle), and NF-UQ-NIDS-v2 (Kaggle, created by combining small datasets).\"},{\"question\":\"What hardware and software requirements are specified for running the project artefact?\",\"answer\":\"Hardware includes Windows 10 or similar OS, Intel i5 8th Gen or above, 16 GB DDR4 RAM, and 50 GB available storage. Software setup uses Microsoft Excel for early exploration and Jupyter Notebook in an Anaconda environment with Python kernel and specific package versions.\"}]","Enhancing Network Security Using Machine Learning Model-Agnostic Approach on Diverse Datasets - Configuration Manual | PDF"]