Projects

IoT Botnet Detection using Machine Learning

Institution: SRM University, Chennai, Tamil Nadu | Published in: ICEC2024

• Tools & Technologies: Python Feature Engineering, Model Evaluation,

Hyperparameter Optimization, Machine Learning Algorithms (Random Forest, SVM),

IoT Security

~~Key Contributions:

-Feature Engineering: Extracted and transformed features from IoT network traffic data to improve model accuracy.

-Model Evaluation: Applied performance metrics like Precision, Recall, F1- Score, and Confusion Matrix to assess the model’s effectiveness in detecting botnet activities.

-Hyperparameter Optimization: Tuned machine learning models using Grid Search and Cross-validation for better detection results.

-Developed and fine-tuned machine learning and deep learning models using scikit-learn and TensorFlow for botnet detection in IoT networks.

-Optimized models for real-time intrusion detection and scalable IoT security solutions.

-Hyperparameter Optimization: Tuned hyperparameters of machine learning models using grid search and cross-validation to achieve optimal detection performance.

-Developed and trained machine learning models using scikit-learn and TensorFlow for the detection of botnet-related anomalies in IoT devices.

-optimized models for real-time intrusion detection, ensuring the solution was scalable and efficient for large IoT environments.