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The material lists required hardware and software, introduces the CICIDS2017 dataset and its characteristics, and outlines objectives for detecting and mitigating cyber attacks. It also provides steps for segregating and normalizing data, including MinMaxScaler scaling, and conceptual guidance on ANN operation via forward and 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Michael Pantridge  \nVideo Link: [https://www.youtube.com/watch?v=nW_3VJhM7fE](https://www.youtube.com/watch?v=nW_3VJhM7fE)  \nIntroduction  \nThe following paper demonstrates how to build a deep learning-based intrusion detection system (IDS) utilizing ANN and GRU models.  \nIt is based on the findings ofthe project \"Safeguarding Financial Transaction: A Machine learning perspective on online payment network security”.  \nSYSTEM PREREQUISITES:  \nHARDWARE REQUIREMENTS  \n• System Processor : Intel i3/ AMD A5  \n• Hard Disk : 500 GB.  \n• Ram : 8 GB / 12 GB.  \n• Any Desktop / Laptop system with the above configuration or higher level.  \nSOFTWARE REQUIREMENTS  \n• Operating system: Windows 10 / 11 (64 bits OS)  \n• Programming Language : Python 3  \n• Framework: Anaconda  \n• Libraries: Keras, TensorFlow, OpenCV  \n• IDE : JupyterNotebook / Pycharm  \n• TOOLS  \no Visual Code ++  \no Anaconda Navigator  \no Jupyter Notebook  \no Tensor flow, Keras  \no Matplotlip, SKLearn  \no Numpy, Pandas  \n• PROGRAMMING LANGUAGES  \nPython 3.4.1  \nDATASET LINK:  \n[https://data.world/dradar/cicids2017](https://data.world/dradar/cicids2017)  \n• CICIDS2017 stands for Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset.  \n• It serves as a benchmark dataset for evaluating intrusion detection systems (IDS) .  \n• The dataset is openly accessible to the general public, facilitating study and assessment.  \n• It includes tagged network traffic captures (PCAPs) and network flow statistics.  \n• PCAPs and flow statistics reveal both benign and malicious network activity.  \n• The primary objective is to assess IDS efficiency in detecting and mitigating cyber attacks.  \n• With large dimensions, the dataset allows for extensive testing of IDS.  \n• Reflecting real-world traffic patterns, CICIDS2017 provides a realistic environment for assessment.  \n• Researchers can utilize it to train and evaluate IDS algorithms in real-world scenarios.  \n• Analyzing network traffic patterns aids in developing more effective IDS.  \n• It serves cybersecurity research by offering a significant resource for testing and enhancing IDS.  \n• By fostering the development of more capable IDS, it contributes to improved cybersecurity posture.  \n• The dataset includes instances of benign network traffic and common cyber-attacks.  \n• Sized at 7.92 MB, it is easy to download and study.  \n• Distributed under a Public Domain license, it allows free use, modification, andredistribution.  \n• Organized as a single file with 79 columns representing various network traffic aspects.  \n• Unlike standard table designs, CICIDS2017 lacks tables, potentially requiring additional preparation for analysis.  \n• Its purpose is to provide real-world examples of network activity for cybersecurity research and analysis.  \n• Accessible nature and comprehensive coverage make it essential for academics, analysts, and practitioners to understand cybersecurity risks, develop intrusion detection models, and enhance network security.  \nSteps to segregate and normalize data in preparation for model utilization:  \nData Segregation:  \nData segregation is essential for the security by partitioning data into distinct components with unique access controls. This tiered strategy diminishes the risk of unauthorized access, data breaches, and leaks, fostering a more secure information environment.  \nCompliance mandates in sectors like banking, healthcare, and personal data handling underscore the necessity of data segregation. These regulations frequently mandate data isolation to safeguard privacy and ensure proper data handling, aiding enterprises in sidestepping legal and financial repercussions.  \nData classification enhances the efficacy of pinpointing, evaluating, and retrieving specific information, particularly within vast datasets. This height","cbCaia8pu1ptQcSl","https://ap.wps.com/l/cbCaia8pu1ptQcSl","pdf",1722822,23,"English","# Introduction\n## System Prerequisites\n## Data Segregation and Normalization\n## Steps to Set Up the ANN and GRU Models","[{\"question\":\"What deep learning approach does the document describe for intrusion detection?\",\"answer\":\"It describes building an intrusion detection system using ANN and GRU models with deep learning.\"},{\"question\":\"What are the system prerequisites listed for running the project?\",\"answer\":\"It specifies hardware requirements (e.g., Intel i3/AMD A5, 500 GB disk, 8 GB/12 GB RAM) and software requirements such as Windows 10/11, Python 3, Anaconda, and libraries including Keras and TensorFlow.\"},{\"question\":\"How is the CICIDS2017 dataset used in this work?\",\"answer\":\"The document positions CICIDS2017 as an openly accessible benchmark dataset containing tagged network traffic captures and flow statistics for evaluating and training intrusion detection models against benign and malicious activity.\"}]","Configuration Manual - MSc Academic Internship Cyber Security | PDF",1789642386,58]