ThreatClassifier

• Tech Stack: Python, Pandas, Scikit-learn, Matplotlib, Seaborn
• Github URL: Project Link

• Project Description: ThreatClassifier is a machine learning-based tool designed to analyze IoT malware datasets and classify network traffic into malicious and benign categories. The project aims to assist in identifying potential threats within IoT networks effectively.
• Data Preprocessing: Combines multiple IoT malware datasets, processes labels, and handles class imbalances through downsampling of overrepresented classes.
• Visualization: Provides visual insights into data distributions, class frequencies, and sampling effects using Seaborn and Matplotlib.
• Target Classification: Implements models like Random Forest and Multi-layer Perceptron (MLP) to classify traffic based on labeled data.
• Class Weights: Calculates and applies class weights dynamically to improve classification accuracy for underrepresented classes.
• Scalability: Processes large-scale IoT datasets and generates actionable insights for threat mitigation.
• Outputs: Delivers classification results, detailed analysis, and visualizations for each target category.