vgj# Security Event Correlation

Project Overview

Our project, titled "Security Event Correlation," focuses on enhancing cybersecurity by developing a machine learning model that correlates and analyzes security events from multiple sources, such as logs and alerts, to detect potential security incidents. The project leverages advanced techniques like event clustering, pattern recognition, and neural networks (RNNs and LSTMs) to automate the analysis of large-scale security data, strengthen cyber defenses, and provide real-time threat detection and response.

Key Features

• Event Clustering: Grouping similar security events to identify patterns and reduce noise.
• Pattern Recognition: Identifying recurring patterns in security data that could indicate potential threats.
• Neural Networks: Utilizing RNNs and LSTMs to model and predict complex security incidents.
• Real-Time Detection: Offering immediate response capabilities by processing and analyzing data in real-time.
• Scalability: Designed to handle large volumes of security data across multiple sources.

Objectives

• To enhance the ability to detect and respond to security incidents by correlating and analyzing data from various sources.
• To automate the processing of overwhelming security data into actionable insights.
• To contribute to the development of more resilient and robust cybersecurity systems.

Technology Stack

• Programming Language: Python
• Libraries: TensorFlow, Scikit-learn, Pandas, NumPy
• Machine Learning Models: RNNs, LSTMs
• Data Sources: Logs, alerts, and other security event data

Getting Started

Prerequisites

• Python 3.x
• TensorFlow
• Scikit-learn
• Pandas

Data Version Control (DVC)

This project uses DVC to manage data files.

Getting Started

1. Clone the repository:

   git clone https://github.com/Boomsnipa/B.Tech-Project-Security-Event-Correlation-
   cd B.Tech-Project-Security-Event-Correlation-
   

2. Install DVC:

   pip install dvc
   

3. Pull the Data:

   dvc pull
   

4. Run the Project:

   python src\main.py