Optimized Deep Feature Analysis for Enhanced Botnet Attack Prediction in IoT Networks

Abstract

The Optimized Deep Feature Analysis (ODFA) framework targets botnet attacks in IoT networks by combining deep learning with machine learning techniques. It uses a hybrid CNN-LSTM model to extract spatial and temporal features from network traffic, applying Recursive Feature Elimination (RFE) with Cross-Validation (CV) for refining feature selection. A Random Forest classifier is then employed to classify different types of botnet attacks. Tests conducted on the UNSW-NB15 dataset demonstrate its ability to achieve high precision, sensitivity, specificity, and accuracy. Recurrent feature optimization allows the framework to adapt to changes in attack patterns, ensuring consistent detection performance. The reduction in false alarms and improved classification of attack types highlights its contribution to securing IoT networks.