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Related Experiment Video

Updated: Jul 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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An improved long short term memory network for intrusion detection.

Asmaa Ahmed Awad1, Ahmed Fouad Ali1,2, Tarek Gaber3,1

  • 1Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.

Plos One
|August 1, 2023
PubMed
Summary
This summary is machine-generated.

A new Improved Long Short-Term Memory (ILSTM) algorithm enhances intrusion detection systems. This novel approach significantly boosts accuracy and precision in identifying network threats compared to traditional methods.

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Intrusion detection systems (IDS) are vital for network security, identifying threats from network traffic.
  • Traditional machine learning methods in IDS often struggle with low accuracy and high false alarm rates.
  • Deep learning models like Long Short-Term Memory (LSTM) show promise but require extensive training.

Purpose of the Study:

  • To propose a novel Improved Long Short-Term Memory (ILSTM) algorithm for enhanced intrusion detection.
  • To improve the accuracy and reduce false alarms in network intrusion detection systems.
  • To develop an efficient IDS capable of handling both binary and multi-class classification of network attacks.

Main Methods:

  • Developed an ILSTM algorithm integrating the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO).
  • Employed a two-phase approach: initial LSTM training followed by CBOA and PSO for weight optimization.
  • Evaluated the ILSTM-based IDS on NSL-KDD and LITNET-2020 datasets using nine performance metrics.

Main Results:

  • The ILSTM algorithm achieved significantly higher accuracy (93.09%) and precision (96.86%) compared to standard LSTM (82.74% accuracy, 76.49% precision).
  • ILSTM demonstrated superior performance over LSTM and other deep learning algorithms across both datasets.
  • Statistical analysis confirmed the greater significance of ILSTM, particularly in multiclassification of intrusion types like DoS, Prob, and U2R.

Conclusions:

  • The proposed ILSTM algorithm offers a substantial improvement for network intrusion detection systems.
  • ILSTM effectively enhances accuracy and precision, outperforming existing deep learning models.
  • This optimized approach provides a more robust and statistically significant solution for identifying diverse network intrusions.