SMARTSEC 🛡️

An AI-powered real-time system monitor and intrusion detection system (IDS) built with Python and Machine Learning.

This project is designed to provide real-time monitoring of system and network activities to detect and flag potential security threats using both anomaly-based and signature-based detection methods.

📖 About The Project

SMARTSEC is a system-monitoring and intrusion detection system that actively monitors a machine's processes and network traffic. It leverages machine learning models to distinguish between normal and malicious activities. The system features a web-based dashboard for visualizing alerts and system status in real-time. This project was developed as a comprehensive tool to apply machine learning concepts to the practical field of cybersecurity.

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✨ Key Features

• Real-Time Monitoring: Agents continuously collect data on running processes and network packets.
• Anomaly Detection: Uses an Isolation Forest model (isolation_forest_model.joblib) to detect unusual patterns that deviate from the norm.
• Signature-Based Detection: Employs a Random Forest model (random_forest_model.joblib) to identify known malicious patterns.
• Web Dashboard: A Flask-powered user interface (index.html) for easy monitoring and viewing of alerts.
• Modular Design: A decoupled agent-server architecture for scalability and maintenance.

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💻 Tech Stack

• Backend: Python, Flask
• Machine Learning: Scikit-learn, Pandas, NumPy
• Data Collection: Psutil (for system processes), Scapy (for network packets)
• Frontend: HTML, CSS, JavaScript
• Deployment: Local server environment

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🏗️ Software Architecture

The system is designed with a central server and multiple agents that can be deployed on hosts to be monitored.

1. Agents (host_agent.py, network_agent.py):

   • These are lightweight Python scripts that run on the target machine.
   • The host_agent collects data about running processes, CPU usage, and memory consumption.
   • The network_agent captures and analyzes network traffic.
   • Both agents send the collected data to the central server via HTTP requests.

2. Central Server (app.py):

   • This is a Flask web server that acts as the core of the system.
   • It exposes an API endpoint (/api/data) to receive data from the agents.
   • It passes the received data to the Detection Engine for analysis.
   • It serves the web dashboard to the user.

3. Detection Engine (detection_engine.py):

   • This component contains the core logic for threat detection.
   • It preprocesses the data and feeds it into the pre-trained machine learning models (Isolation Forest and Random Forest).
   • If a threat is detected, it generates an alert that is displayed on the dashboard.

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🚀 Getting Started

Follow these steps to get a local copy up and running.

Prerequisites

• Python 3.10+
• Git

Installation

1. Clone the repository

   git clone [https://github.com/krishmishra09/SMARTSEC.git](https://github.com/krishmishra09/SMARTSEC.git)
   cd SMARTSEC

2. Create and activate a virtual environment

   # For Windows
   python -m venv venv
   .\venv\Scripts\activate

3. Install the required packages

   pip install -r requirements.txt

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Krish Mishra - [https://www.linkedin.com/in/krish-mishra-b0aa27295/]

Project Link: [https://github.com/krishmishra09/SMARTSEC]