Energy-Efficient Bio-Inspired Hybrid Deep Learning Model for Network Intrusion Detection Based on Intelligent Decision Making


  • Sandeep Kumar Hegde Associate Professor, Department of Computer Science and Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka
  • P. William Department of Information Technology, Sanjivani College of Engineering, Kopargaon, SPPU, Pune
  • Mule Shrishail Basvant Associate Professor, Department of Electronics &Telecommunication Engineering, Sinhgad College of Engineering, Pune-41
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Arti Badhoutiya Department of Electrical Engineering, GLA University, Mathura
  • A L N Rao Lloyd Institute of Engineering & Technology, Greater Noida
  • Amit Srivastava Lloyd Law College, Greater Noida
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu


Network Security, Bio-Inspired Algorithms, Hybrid Deep Learning, Energy Efficiency, Intelligent Decision Making, Resource Optimization, Computational Efficiency, Anomaly Detection, Deep Learning


This study presents an energy-efficient, hybrid deep learning model for network intrusion detection that takes its cues from biology. As a result, intelligent decision-making is included into the improvement of security infrastructures. The model's high accuracy is shown by the fact that it performs well across a variety of metrics, including TPR, precision, and F-Measure, thanks to the use of deep learning and techniques inspired by biology. Despite the fact that there is some unpredictability in FPR and FNR, the data demonstrate that the model is capable of providing a long-term and intelligent response to the ever-changing cybersecurity threats


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How to Cite

Hegde, S. K. ., William, P. ., Basvant, M. S. ., Deepak, A. ., Badhoutiya, A. ., Rao, A. L. N. ., Srivastava, A. ., & Shrivastava, A. . (2024). Energy-Efficient Bio-Inspired Hybrid Deep Learning Model for Network Intrusion Detection Based on Intelligent Decision Making. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 306 –. Retrieved from



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