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Machine Learning Approaches to Detect Application Layer DDoS Attacks

Year: 2026

Type: conference-paper

Abstract: 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.

Source: International Scientific Conference TechSys — Engineering, Technology and Systems (TechSys)

DOI: 10.3390/engproc2026150120

Authors: Ali Sabra, Nehmeh Rmeiti, Zlatogor Minchev

Institutions: Varna Free University, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7201862172

FWCI:

Cites: 12

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Related to: 10