ARGUS is an AI-powered autonomous network defense appliance that continuously monitors network traffic, detects malicious activity using machine learning, and automatically responds to threats in real time.
ARGUS stands for Autonomous Real-time Gateway Unsupervised Shield.
The name reflects the system's purpose as a continuously vigilant cybersecurity platform capable of autonomously monitoring, detecting, and responding to network threats in real time.
Cyber threats such as port scanning, denial-of-service attacks, botnet activity, and unauthorized network reconnaissance continue to increase across educational institutions, small businesses, hospitals, and rural infrastructures.
Most advanced cybersecurity solutions are expensive, complex to deploy, and require dedicated security personnel.
Organizations with limited cybersecurity expertise often remain vulnerable to attacks.
ARGUS introduces a lightweight and scalable network defense appliance capable of:
- Monitoring network traffic continuously
- Extracting meaningful packet-level features
- Detecting anomalies using Machine Learning
- Classifying malicious activities
- Automatically responding to threats
- Providing real-time monitoring and alerting
Captures and analyzes network traffic continuously.
Uses anomaly detection models to identify suspicious behavior.
Initiates defensive actions against malicious IP addresses.
Provides real-time visibility into network activity and detected threats.
Designed for deployment as a dedicated cybersecurity appliance or direct router integration.
Network Traffic
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Packet Capture
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Feature Extraction
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Machine Learning Detection
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Threat Classification
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Automated Response Engine
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Real-Time Streamlit Dashboard
Incoming Network Packets
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Traffic Capture
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Packet Analysis
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Feature Extraction
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Anomaly Detection
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Threat Classification
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Alert Generation
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Automatic Defensive Action
The current implementation operates on a development workstation for demonstration and validation purposes.
This prototype validates the complete ARGUS detection pipeline while the future product vision targets deployment on:
- Dedicated cybersecurity appliances
- Embedded hardware platforms
- Router-integrated environments
- Enterprise edge-security solutions
The current implementation is a software prototype demonstrating the complete ARGUS detection pipeline. Future versions are intended for deployment as dedicated cybersecurity appliances and router-integrated security solutions.
src/
- Detection engine and dashboard modules
models/
- Trained Machine Learning Models
deployment/
- Platform-specific launch scripts
docs/
- Architecture and system documentation
hardware/
- Future deployment roadmap
- Python
- Scikit-Learn
- Pandas
- NumPy
- Scapy
- Bettercap
- Streamlit
- Machine Learning
Clone the repository:
git clone https://github.com/YKMadhav/ARGUS.git
cd ARGUSInstall the required dependencies:
pip install -r requirements.txtLaunch the Streamlit dashboard:
streamlit run app.pyAfter launching the dashboard, open the local Streamlit URL displayed in the terminal (typically http://localhost:8501) to begin monitoring network traffic.
ARGUS has been tested on the following operating systems:
- macOS
- Windows
Compatibility with Linux has not yet been verified.
ARGUS aims to evolve from a software prototype into a self-contained cybersecurity appliance capable of protecting organizations that lack access to enterprise-grade security infrastructure.
- Khatwang Madhav Yippili
- Karthikeya Bodanki