Detecting IoT Attacks Using Adversarial Machine Learning

The Internet of Things (IoT) has become increasingly susceptible to cyber attacks, making it crucial to have strong detection mechanisms. This paper looks at using adversarial machine learning to boost IoT security. We implemented and evaluated Feedforward Neural Networks (FNN) and Long Short-Term Memory (LSTM) models on the Bot-IoT dataset for binary and multi-class classification tasks. We then evaluated how these models perform under Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) adversarial attacks. Our results show high accuracy in detecting attacks under normal conditions. However, the models showed major weaknesses when faced with adversarial examples. This study highlights the urgent need for building adversarially robust machine learning models for IoT security. It also gives insights into how different model architectures perform against various attack intensities.