Title: Deep learning prediction model for DoS and SQL injection attack in SDN

Authors: Rejo Rajan Mathew; Amarsinh Vidhate

Addresses: Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil University, Nerul, Navi Mumbai, India ' Department of Computer Engineering, Ramrao Adik Institute of Technology, D.Y. Patil University, Nerul, Navi Mumbai, India

Abstract: The overdependence on data in the digital ecosystem has introduced significant cybersecurity challenges, making traditional intrusion detection systems (IDS) increasingly inadequate, particularly against novel or evolving threats. This paper studies the effectiveness of deep learning (DL) techniques - specifically gated recurring units (GRU), long-short-term memory (LSTM) networks and their hybrid configurations - in detecting distributed denial of service (DDoS) and SQL injection attacks without relying on predefined intrusion signatures. Through extensive experimentation with individual and combined DL models, hybrid approaches demonstrated superior performance compared to conventional IDS across key evaluation metrics, including accuracy, precision, recall, and F1-score. Notably, the RNN+LSTM model achieved an accuracy of 95.14% for DDoS detection and 99.20% for SQL injection detection, outperforming traditional IDS in both cases. These results underscore the potential of advanced DL-based approaches in addressing the limitations of conventional systems and enhancing the real-time detection of advanced threats.

Keywords: intrusion detection system; IDS; deep learning; neural networks; denial of service attack; SQL injection attack.

DOI: 10.1504/IJICS.2025.149446

International Journal of Information and Computer Security, 2025 Vol.28 No.3, pp.327 - 347

Received: 15 Dec 2024
Accepted: 30 Apr 2025
Published online: 31 Oct 2025 *

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