BotNet-Inspired HTTP-Based DDoS Attack Prevention Using Supervised Machine Learning Algorithms in Internet of Things Devices

DDoS attacks have emerged as the most serious Internet risks, causing significant damage to businesses and governments. This study utilized the BoT-IoT dataset obtained from the University of New South Wales (UNSW) website on machine learning (ML) algorithms to prevent HTTP-based DDoS attacks in the Internet of Things environment. Character encoding was performed to convert the strings such as DDoS attack to 1 and Non-malicious to 0. Feature scaling and normalization were carried out to normalize numerical data with large values using a Python module called standard scaler to avoid model overfitting or underfitting. According to the performance evaluation metrics results, decision tree is the best model with 99.96% accuracy, 0.9979 R
²
, 0.0003 MAE, 0.0003 MSE, 0.0195 RMSE, 1.0 precision, 0.9994 recall, 0.9997 F1_score after dimensionality reduction. After comparing various ML models, this study concludes that the decision tree is the golden model for HTTP-based DDoS attack prevention in IoT devices. The study recommends the utilization of other performance evaluation metrics such as Matthew Correlation Coefficient (MCC), Kappa Statistic and Huber loss for further studies to avoid bias and subjectivity in the model selection where there are surrogate models. Also, deep learning paradigms can be implemented for the prevention of HTTP-based DDoS attacks in cloud environments.