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

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 IoT network. To address the problem, a new framework model is proposed. Firstly, a novel feature selection metric approach named CorrAUC proposed, and then based on CorrAUC, a new feature selection algorithm name Corrauc is develop and design, which is based on wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using AUC metric. Then, we applied 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 dataset and four different ML algorithms. Experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average