The Network Anomaly Detection System (NADS) has been developed to enhance network
monitoring and security by transitioning from traditional manual detection methods to an
automated, AI-driven approach. This system leverages modern technologies to ensure efficiency,
accuracy, and scalability in detecting and addressing network anomalies.
NADS aims to save time, effort, and resources for network administrators and users by offering a
user-friendly interface and robust functionality. Feedback on existing systems and their limitations
was gathered through surveys to design a solution tailored to user needs.
The system has been developed in two main parts:
A Streamlit-based web interface for real-time interaction, allowing administrators to monitor
network traffic, view anomaly alerts, and generate reports.
A backend powered by TShark for network traffic analysis, coupled with a Keras-based deep
learning model to detect anomalies with high accuracy.
Data preprocessing and analysis are managed using Pandas, while Plotly is employed to visualize
packet capture and anomaly patterns interactively. The integration of these tools enables the
system to process large volumes of data efficiently and provide actionable insights to users.
In the future, NADS aims to expand its capabilities by incorporating advanced machine learning
models and additional features to support broader network environments, ensuring comprehensive
protection against evolving security threats.
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Install dependencies: pip install -r requirements.txt