AI-Driven-BTIS-Design

An intelligent AI-Driven BTIS (Behavior Threat Intelligence System) that leverages machine learning to monitor system activities, analyze user behavior, and proactively detect security threats.

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AI-Driven BTIS Design

AI-Driven BTIS Design is an intelligent system that uses Artificial Intelligence techniques to assist in the design and development of a BTIS (Behavior-Threat Intelligence System). The goal of this project is to automate security monitoring, analyze behavioral patterns, and detect potential threats in a digital environment using machine learning and intelligent data processing.

The system integrates modern AI techniques with cybersecurity concepts to improve threat detection, decision making, and system monitoring.

Project Overview

Cyber threats are increasing rapidly in modern network environments. Traditional security systems often fail to detect advanced threats or abnormal user behavior in real time.

AI-Driven BTIS Design focuses on building a smart security framework that can analyze network activities, identify suspicious behavior, and provide automated insights for administrators.

Artificial Intelligence and machine learning techniques are widely used in modern software engineering to automate complex tasks, improve prediction accuracy, and support intelligent decision making in software systems. ([ICCK][1])

This project demonstrates how AI techniques can be applied to enhance cybersecurity monitoring and threat detection systems.

Key Features

  • AI-based threat detection
  • Behavioral analysis of system activities
  • Intelligent monitoring of network events
  • Real-time data processing
  • Scalable security monitoring architecture
  • Modular system design for future improvements

System Architecture

The system is designed using a modular architecture where different components handle specific tasks such as:

  • Data Collection
  • Network Monitoring
  • Feature Extraction
  • AI-Based Threat Detection
  • Alert Generation

Each module works together to provide a complete intelligent security monitoring system.

Technologies Used

The project is developed using modern technologies including:

  • Python
  • Machine Learning
  • Data Processing Techniques
  • Network Monitoring Concepts
  • AI-based Threat Analysis

Project Structure

AI-Driven-BTIS-Design
│
├── backend
│   ├── api
│   ├── ml
│   ├── sniffer
│   └── main.py
│
├── src
│   ├── components
│   ├── sections
│   └── ui
│
├── assets
├── README.md
└── configuration files

How to Run the Project

1. Clone the Repository

git clone https://github.com/akramlatif/AI-Driven-BTIS-Design.git

2. Navigate to the Project Folder

cd AI-Driven-BTIS-Design

3. Install Dependencies

pip install -r requirements.txt

4. Run the Application

python main.py

Future Improvements

  • Advanced AI threat detection models
  • Real-time security dashboard
  • Automated response system for detected threats
  • Integration with cloud security platforms
  • Improved visualization of network traffic

Screenshots

Dashboard Overview

BTIS Dashboard Overview

Security Alerts

BTIS Alerts Page

Users at Risk

BTIS Users at Risk

Behavior Analysis

BTIS Behavior Analysis

ML Models

BTIS ML Models

Email Alert - High Risk User Detected

BTIS Email Alert

Author

Akram Latif Cyber Security Student

License

This project is developed for educational and research purposes.

[1]: https://www.icck.org/article/abs/jse.2025.407864?utm_source=chatgpt.com "Towards AI-Augmented Software Engineering: A Theoretical Framework"

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