CLASSIFYING IMBALANCED DATA BASED ON DOS AND DDOS ATTACKS
International Research Journal of Modernization in Engineering Technology and Science · 2023
In the first quarter of 2022, researchers witnessed over 2.8 million Distributed Denial of Service (DDoS) attacks, as reported by Info Security magazine on May 18, 2021.DDoS has become a serious issue for many organizations and individuals.Machine learning algorithms (MLAs) have become a tool to help thicken the layers of defense.To be effective, MLAs must be trained in ways that provide high confidence for detection and prevention.The goal is to develop models that can predict (i.e., classify) with high precision and accurately identify different types of DoS/DDoS attacks with low false positive/negative rates.In addition to dealing with the multiclass classification and extremely imbalanced problems.The derived model leverages two feature selection techniques to reduce the number of features and help improve the model's execution time.A combination of under-sampling combined with adjusting weights was applied to handle the imbalance problem.The extracted data was evaluated using supervised MLAs, including Random Forest, Decision tree, and Logistic regression.The experiments utilized the popular benchmark called NSL-KDD dataset.Random Forest achieved the best performance results, decreasing by 37% the training and testing time.In addition to solving the imbalance problem, it increased accuracy by 6.25% and FPR 21%.The random forest model has achieved 99% accuracy and 0.0001 for the False-Positive rate.Furthermore, using this setup, we can detect minor classes with more than 80% accuracy.