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Cloud-based DDoS detection using hybrid feature selection with deep reinforcement learning (DRL).

Suneeta Satpathy1, Uttpal Tripathy2, Pratik Kumar Swain3

  • 1Centre for Cybersecurity, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, India. suneetasatapathy@soa.ac.in.

Scientific Reports
|October 21, 2025
PubMed
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This study introduces a deep reinforcement learning (DRL) framework for real-time detection of Distributed Denial of Service (DDoS) attacks in cloud environments. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm demonstrated superior accuracy and speed for adaptive threat detection.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Distributed Denial of Service (DDoS) attacks increasingly threaten cloud infrastructure.
  • Traditional Intrusion Detection Systems (IDSs) struggle with the dynamic nature of modern cyber threats.
  • There is a need for adaptive and real-time DDoS detection solutions in cloud environments.

Purpose of the Study:

  • To propose and evaluate a deep reinforcement learning (DRL)-based framework for real-time DDoS detection in cloud computing.
  • To compare the performance of three actor-critic DRL algorithms: TD3, DDPG, and A2C.
  • To develop a robust hybrid feature selection strategy for enhanced detection accuracy and interpretability.

Main Methods:

  • Implemented a DRL framework utilizing TD3, DDPG, and A2C algorithms for network traffic classification.
Keywords:
A2CActor-CriticCICDDoS2019Cloud computingDDPGDDoS detectionIntrusion detectionNetwork securityReinforcement learningTD3

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  • Employed a hybrid feature selection method combining Boruta, SHAP, and cross-validation stability.
  • Trained and evaluated models on CICDDoS2019 and UNSW-NB15 datasets with preprocessing and class imbalance handling.

    • Main Results:

    • The TD3 algorithm achieved the highest performance with 99.12% accuracy and 99.21% AUC.
    • Inference latency was as low as 1.87 milliseconds per sample, suitable for real-time applications.
    • SHAP-based analysis provided interpretability by identifying key features influencing DDoS detection.

    Conclusions:

    • The proposed DRL-based framework, particularly TD3, offers an effective, scalable, and interpretable solution for real-time DDoS detection.
    • This approach overcomes the limitations of traditional IDSs in dynamic cloud environments.
    • The hybrid feature selection and DRL model provide an adaptive defense against evolving cyber threats.