HYBRID MECHINE LEARNING MODEL FOR EFFICIENT BOTENT ATTACK DETECTION IN IOT ENVIRONMENT

Abstract

The rapid growth of Internet of Things (IoT) devices has significantly increased the risk of cyber-attacks, particularly botnet attacks that exploit vulnerable devices to launch distributed malicious activities. Traditional security mechanisms are often insufficient to detect such sophisticated and evolving threats. This project proposes a Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment, which combines multiple machine learning techniques to enhance detection accuracy and robustness. The system analyzes network traffic data generated by IoT devices to identify malicious patterns and classify them as normal or botnet activity. The proposed model integrates algorithms such as Random Forest, Support Vector Machine (SVM), and Neural Networks to form a hybrid framework that leverages the strengths of each method. Data preprocessing techniques including feature extraction, normalization, and dimensionality reduction are applied to improve model performance. The hybrid model uses ensemble learning and weighted voting mechanisms to produce accurate predictions. Additionally, real-time monitoring capabilities are incorporated to detect attacks promptly and minimize damage. The performance of the system is evaluated using metrics such as accuracy, precision, recall, F1-score, and detection rate. Experimental results demonstrate that the hybrid approach outperforms individual models in detecting botnet attacks with higher accuracy and lower false positive rates. The system provides a scalable and efficient solution for securing IoT environments against cyber threats. Overall, this project highlights the importance of combining machine learning techniques to build intelligent and adaptive security systems capable of handling complex attack patterns in modern IoT networks.