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Updated: Jan 12, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Deep reinforcement learning-based intrusion detection scheme for software-defined networking.

R Kanimozhi1, P S Ramesh2

  • 1Department of Artificial Intelligence and Data Science, A.V.C. College of Engineering, Mayiladuthurai, Tamilnadu, India. kanimozhivedharajan@gmail.com.

Scientific Reports
|November 5, 2025
PubMed
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A novel Deep Reinforcement Learning-based Intrusion Detection Scheme (DRL-IDS) enhances Software-Defined Networking (SDN) security. This advanced system effectively detects and mitigates diverse cyber threats with high accuracy and adaptability.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • Software-Defined Networking (SDN) presents unique security challenges due to its centralized control plane.
  • Distributed Denial of Service (DDoS) attacks and network misbehaviors pose significant threats to SDN environments.
  • Existing intrusion detection systems often struggle with the dynamic nature and complexity of modern networks.

Purpose of the Study:

  • To develop and evaluate a robust Deep Reinforcement Learning-based Intrusion Detection Scheme (DRL-IDS) for enhanced SDN security.
  • To integrate Long-Short Term Sequence Recurrent Neural Network (LFTS-RNN) for accurate attack detection and Particle Cloud-Integrated Joint Time- and Feature-Optimization Algorithm (PC-JTFOA) for network management.
  • To improve the detection and mitigation of a wide array of cyber threats, including DDoS attacks and network misbehaviors.
Keywords:
Deep reinforcement learningDistributed denial of service attack detectionIntrusion detection systemLong short-term memory networkParticle colony-adjusted jumping teaching fishing optimization algorithmSoftware-defined networking

Related Experiment Videos

Last Updated: Jan 12, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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Published on: December 15, 2023

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Main Methods:

  • A hybrid model combining LFTS-RNN for threat identification and PC-JTFOA for feature selection, load balancing, and energy-efficient routing.
  • Utilizing deep reinforcement learning for continuous adaptation to evolving network behaviors and emerging attack vectors.
  • Experimental validation using the NSL-KDD and WPPD datasets to assess performance.

Main Results:

  • The LFTS-RNN model achieved high sensitivity (98.67%) and specificity (97.42%).
  • The DRL-IDS scheme demonstrated superior detection accuracy (99.85%) and adaptability.
  • The PC-JTFOA component resulted in a low response time (1423 ms), indicating improved computational efficiency.

Conclusions:

  • The proposed DRL-IDS scheme significantly outperforms existing intrusion detection methods in accuracy and adaptability.
  • The hybrid approach effectively addresses security vulnerabilities across different SDN planes.
  • The DRL-IDS offers a computationally efficient and robust solution for securing SDN environments against diverse cyber threats.