Intrusion Detection using IoT with Deep Learning

The rapid expansion of the Internet of Things (IoT) has led to a surge in interconnected devices, increasing the risk of cyber threats. Traditional Intrusion Detection Systems (IDS) often fail to handle the complexity and dynamic nature of IoT network traffic. This paper introduces a hybrid deep learning model that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN) to enhance intrusion detection performance. Trained on the BoT-IoT dataset, the proposed system achieves high accuracy and reliability, demonstrating its effectiveness in identifying malicious activity in IoT environments.