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

TLDR

Anomaly and malicious traffic detection is critical for IoT security, yet many ML models misclassify due to poor feature selection. The study aims to investigate how to select effective features for accurate malicious traffic detection in the IoT network and proposes a new framework model. The authors propose CorrAUC, a novel feature selection metric and algorithm based on a wrapper technique and AUC, combined with TOPSIS and Shannon entropy on a bijective soft set, and evaluate it on the Bot‑IoT dataset with four ML algorithms. Experimental results show the method achieves over 96 % accuracy on average.

Abstract

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 feature selection metric approach named CorrAUC is proposed, and then based on CorrAUC, a new feature selection algorithm named CorrAUC is developed and designed, which is based on the wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using the area under the curve (AUC) metric. Then, we applied the integrated TOPSIS and Shannon entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT data set and four different ML algorithms. The experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.