IOT BOTNET DETECTION SYSTEM USING DEEP LEARNING

Project Algorithm :

Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) / LSTM

Project Overview :

The rapid adoption of Internet of Things (IoT) devices has created new opportunities but also introduced severe security threats. Due to limited computational resources and weak authentication mechanisms, IoT devices are vulnerable to botnet attacks, where compromised devices are hijacked to launch Distributed Denial of Service (DDoS) or other malicious activities. Traditional signature-based detection systems are inadequate in identifying novel and evolving botnet traffic. This project proposes an IoT Botnet Detection System using Deep Learning, which leverages neural networks to analyze network traffic patterns and identify malicious activities. The system is capable of detecting botnet attacks with high accuracy by learning hidden traffic patterns from IoT datasets such as UNSW-NB15, Bot-IoT, or N-BaIoT. The project provides a robust framework for real-time monitoring, anomaly detection, and prevention of large-scale IoT botnet attacks.

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