Threat-Detection-System

Project Overview

The "Threat Detection System" utilizes machine learning to enhance predictive threat intelligence in cybersecurity. Leveraging the NSL-KDD dataset, this system employs a RandomForest classifier to identify and predict various types of network intrusions and attacks, addressing the urgent need for dynamic and predictive security measures.

Objective

To develop an effective machine learning model for predicting potential cybersecurity threats, focusing on pattern recognition, anomaly detection, and the potential to generalize to predict unseen attack patterns.

Scope

This project encompasses the application of machine learning techniques to the NSL-KDD dataset for network intrusion detection, including comprehensive data preprocessing and rigorous model evaluation using metrics such as accuracy, precision, recall, and F1 score.

Methodology

• Utilization of the NSL-KDD dataset, an improved version of KDD'99, to avoid redundant and biased records.
• Preprocessing steps including data cleaning, feature encoding, scaling, and selection.
• RandomForest classifier was chosen due to its effectiveness in classification tasks and robustness against overfitting.
• Evaluation of the model's performance through accuracy, precision, recall, F1 score, and a confusion matrix.

Data Preprocessing and Analysis

Detailed preprocessing steps and exploratory data analysis were performed to ensure the data was ready for the machine learning model.

Model Training and Evaluation

• Explanation of the RandomForest algorithm and its suitability for the project.
• The training process involves preprocessed data, handling of imbalanced data, and parameter tuning.
• Performance evaluation through visualizations like ROC and Precision-Recall curves, highlighting the model's strengths and areas for improvement.

Results and Insights

• The model's performance was measured using various metrics, with visualizations provided for an in-depth analysis.
• Feature importance visualization and SHAP value visualization are included to indicate the model's reliance on different features.

Visualizations Included

• Receiver Operating Characteristic (ROC) curves for multi-class classification.
• Precision-Recall (PR) curves for different classes.
• Feature importance and SHAP value visualizations.

Discussion and Future Work

• Insights from the model's performance with respect to class-specific predictions.
• Identified limitations and recommendations for future enhancements, such as improved data collection, feature engineering, and exploration of different algorithms.

Conclusion

The study highlights both the potential and the challenges of applying machine learning to cybersecurity, providing a foundation for future research in predictive threat intelligence.

Visualizations

Visualizations are an integral part of the project and are used to communicate the model's performance and feature importance. Below are the included visualizations: ![ROC Curve for Multi-Class] ![Multi-class ROC Curve for Valid Classes] ![Feature Importance Visualization] ![SHAP Value Visualization] ![Precision-Recall Curve] ![PR Curve Per Class] ![Extension of Precision-Recall Curve to multi-class]

This README is intended to provide a high-level understanding of the project, its objectives, methodologies, and outcomes. For a more detailed explanation, refer to the project report and code within the repository.