DDOS Detection Using ML and Deep Learning Approaches

DDoS has a huge potential of threat towards the availability and performance of webservices. The attack system makes the systems unavailable to the valid users by flooding them with malignant traffic. Traditional defense mechanisms are mostly ill-designed to counter such fast-changing and increasing attacks, so service disruption, financial loss, and reputational damage are expected results. This work explores the potential of real-time DDoS attack detection and mitigation using machine learning and deep learning, with a particular emphasis on scalability, accuracy, and adaptability. The research involves the creation and assessment of several machine learning (ML) methods, such as Random Forests and Decision Trees, in addition to deep learning (DL) models like Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. The study attempts to determine the best models for real-time DDoS detection by comparing and contrasting these methods. The methodology consists of comprehensive data collecting and preprocessing, model building, training, and evaluation. The performance of the models on parameters such as detection speed, accuracy, and resource efficiency is compared and analyzed.