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A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based

Ripal Ranpara1, Osamah Alsalman2, Om Prakash Kumar3

  • 1Faculty of Computer Applications, Marwadi University, Rajkot, 360003, India. ripal.ranpara@marwadieducation.edu.in.

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Summary
This summary is machine-generated.

GreenMU enhances intrusion detection systems by balancing energy efficiency and accuracy using machine learning. This novel framework significantly improves detection rates while reducing energy consumption for resource-constrained environments.

Keywords:
CybersecurityGreen artificial intelligenceIntrusion detection systemsMachine learningSimulative based optimizationSmart algorithms

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning
  • Green Computing

Background:

  • Intrusion detection systems (IDS) face challenges in balancing detection performance with energy efficiency.
  • Increasingly complex cyber threats require sophisticated yet resource-aware security solutions.
  • Resource-constrained environments like IoT and edge computing demand energy-efficient security frameworks.

Purpose of the Study:

  • To propose GreenMU, a novel framework addressing energy efficiency and detection performance in intrusion detection systems.
  • To integrate machine learning, knowledge distillation, and adaptive energy-aware optimization for enhanced cybersecurity.
  • To develop the MUGuard algorithm for dynamic computational complexity adjustment based on energy and threat levels.

Main Methods:

  • Integration of Random Forest and Support Vector Machines classifiers.
  • Application of knowledge distillation and adaptive energy-aware optimization techniques.
  • Development and implementation of the MUGuard algorithm for real-time adaptive processing.

Main Results:

  • GreenMU achieved a detection accuracy close to 99% on the KDD 1999 dataset, surpassing baseline models.
  • Demonstrated a 31% reduction in energy consumption compared to standard models.
  • Improved computational efficiency by reducing processing time by 15%.

Conclusions:

  • GreenMU offers a scalable, sustainable, and high-performing solution for modern intrusion detection.
  • The framework effectively balances computational efficiency and cybersecurity accuracy.
  • Highlights the potential of green artificial intelligence in advancing cybersecurity for resource-constrained environments.