{"repo":"anacletu/ml-intrusion-detection-cicids2017","free":true,"listed":false,"github":"https://github.com/anacletu/ml-intrusion-detection-cicids2017","clone":"git clone https://github.com/anacletu/ml-intrusion-detection-cicids2017.git","description":"Machine Learning-based Intrusion Detection System (IDS) tailored for resource-constrained networks","language":"Jupyter Notebook","stars":20,"topics":["cybersecurity","data-analysis","data-science","data-visualization","ids","machine-learning","machine-learning-algorithms","nids","numpy","pandas"],"license":"MIT","category":"machine-learning","readme_excerpt":"Anomaly-Based NIDS for Resource-Constrained Networks using Machine Learning This repository contains the code, documentation, and resources for my MBA's thesis project, \"Anomaly-based intrusion detection in resource-limited networks.\" The project focuses on developing a practical Network Intrusion Detection System (NIDS) tailored for environments with limited computational resources, such as those found in small businesses or IoT deployments, typically utilizing devices like the Raspberry Pi. The research leverages the CICIDS2017 dataset for model training and initial evaluation, with a final prototype validated using real network traffic captures. View the Full Thesis (Soon) Project Overview The primary goal of this research was to explore and develop an effective, yet lightweight, machine learning-based NIDS suitable for deployment on resource-constrained hardware. Traditional NIDS solutions often demand significant processing power and memory, making them impractical for small businesses or edge devices. This project investigates various machine learning algorithms, ultimately developing a prototype that balances detection accuracy with operational efficiency. A key finding of this research was the superior real-world applicability of instance-based learners like K-Nearest Neighbors (KNN) in dynamic network environments. While tree-based ensemble models like XGBoost demonstrated high accuracy on the curated CICIDS2017 dataset during training, the KNN model proved more adap","default_branch":null,"files":null,"tree":[],"storefront":"/r/anacletu","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/anacletu/ml-intrusion-detection-cicids2017/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}