CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine-learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in the IoT network. To address the problem, a new framework model is proposed. First, a novel…

• NN
  National Natural Science Foundation of China

  Awards: 61871140, 2019B010137004, 2018YFB0803504