Network traffic anomaly and congestion prediction

Network Anomaly Detection & Congestion Prediction System This tool is designed to help you analyze and visualize your network traffic data to identify unusual patterns (anomalies) and predict potential network congestion. Whether you’re a network admin, a data scientist, or simply curious about traffic analysis, this app has you covered. Key Features: Anomaly Detection: Identify abnormal network traffic based on various metrics like packet length, protocol type, and time. Congestion Prediction: Predict potential network congestion by analyzing traffic patterns and detecting anomalies. Interactive Visualizations: Stunning charts and graphs for a more insightful analysis of the data, anomalies, and congestion. Technologies Used: Streamlit: The backbone of this web app, providing an interactive and easy-to-use interface. Pandas: For data manipulation and analysis of the network traffic data. NumPy: For numerical operations and data transformations. Plotly: Interactive and dynamic visualizations like histograms, scatter plots, and pie charts. Scikit-learn: Machine learning models like Isolation Forest for anomaly detection and congestion prediction. Python: The main programming language behind the app’s backend logic. Steps to Use: Step 1: Upload your network traffic data (CSV format). Step 2: Run Anomaly Detection to detect abnormal traffic patterns. Step 3: Predict Network Congestion based on detected anomalies. Visual Enhancements: Anomaly Distribution: A histogram to visualize how many records are normal and how many are anomalous. Anomalies Over Time: Scatter plot shows anomalies in relation to the time of day. Anomaly vs Normal: A pie chart to compare the number of normal vs anomalous records.