A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks

Abstract

Distributed denial of Service (DDOS) attacks are the most common words used to describe the distributed network attacks. These attacks benefit from some restrictions that apply to the property of each scheme, including the structure of the authorized organization's website. The author worked with an old KDD data set for the current study. To determine the current status of the DDOS attacks, the latest dataset must be used. This study classified and predicts what types of DDO's attacks using machine learning techniques. For this, random forest and XGBoost classification algorithms were used. A comprehensive function was proposed to predict DDO's attacks to reach research. Python was used as a simulator for the proposed work, and the UNWS NP-15 dataset was taken from the GitHub source. We created an illusion matrix to identify the model performance after using the machine learning model. According to the results, the early classification consisted of Random Forest algorithm's precision (PR) and recall (RE) and 89%. The average accuracy (AC) for our proposed models is 89%, which is excellent and adequate. According to the results, XGBoost algorithm's Precision (PR) and Recall (RE) about 90% in the second classification. The average accuracy (AC) for our proposed model is 90%. Previous research studies had increased the accuracy of misdiagnosis by comparing our work, which is around 85% and 79%, respectively.