A deep reinforce learning-based intrusion detection method for safeguarding Internet of Things
Liang Zhang, Hongjie Lv · 2023
With the increasing number of Internet of Things (IoT) access devices, security incidents in the IoT field have occurred frequently in recent years. Traditional intrusion detection techniques can no longer meet the current cyber threat discovery needs in the IoT environment. For this reason, we proposed a deep reinforcement learning (DRL)-based IoT intrusion detection system using an intrusion detection dataset produced by an IoT-based platform to enhance network security in the IoT environment in this study. The system applied a feature selection algorithm based on Pearson's correlation coefficient to extract the most effective feature set, applied a multilayer perceptron containing four hidden layers as the deep neural network structure shared by the value network and the policy network in this intrusion detection system, and constructs an intrusion detection system based on DRL proximal policy optimization algorithm. Based on the experimental results show that the proposed intrusion detection system obtains 99.96% accuracy in the face of different network attacks on IoT and outperforms current deep learning models based on LSTM, CNN, and DQN in terms of accuracy, precision, recall, and F1-measure.