Distributed Denial of Service (DDoS) attacks pose a significant threat to online systems by overwhelming target servers with illegitimate traffic. Traditional signature-based detection methods struggle with evolving attack patterns. This paper proposes the use of Artificial Intelligence (AI) and deep learning techniques—particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)—to analyze network traffic and detect anomalous behaviors in real time. The results demonstrate the effectiveness …