This study proposes a machine learning and deep learning framework to detect application layer DDoS attacks using web server access logs. A realistic dataset was generated using eight DDoS tools, along with benign traffic. Multiple ML models, including Random Forest, SVM, and KNN, as well as DL models such as ANN and LSTM, were evaluated. The results show high detection performance, with the LSTM achieving up to 99% accuracy and outperforming other models. Additional validation on non-DDoS attacks revealed that ML models performed poorly, while LSTM maintained strong performance (92.89%). The findings highlight the effectiveness of log-based datasets and the superiority of LSTM for handling sequential and mixed-feature data in cybersecurity detection tasks.