CNN-LSTM Based Approach for DDoS Detection

Distributed Denial of Service (DDoS) attacks have become increasingly common, causing financial and reputational losses for organizations. Despite the existence of numerous conventional detection solutions, DDoS attacks continue to rise in frequency, demanding effective models to detect and prevent them. This paper focuses on developing a machine learning-based approach for DDoS attack detection. By leveraging the power of machine learning, we aim to overcome the limitations of existing methods and propose a novel solution. Our work emphasizes the importance of exploring advanced models and techniques to enhance detection accuracy and efficiency. Through rigorous experimentation, we demonstrate the effectiveness of our approach in proactive defense against real-world DDoS attacks.