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Updated: Sep 28, 2025

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Computational Intelligence Approaches in Developing Cyberattack Detection System.

Mohammed Saeed Alzahrani1, Fawaz Waselallah Alsaade1

  • 1College of Computer Science and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa, Saudi Arabia.

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|March 28, 2022
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Summary
This summary is machine-generated.

This study introduces an intelligent cybersecurity system using machine learning to detect network intrusions, particularly for Internet of Things (IoT) devices. The k-nearest neighbor (KNN) and long short-term memory (LSTM) algorithms showed high accuracy in identifying malicious network traffic.

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning Applications

Background:

  • Intrusions compromise system integrity, privacy, and accessibility.
  • The Internet of Things (IoT) and networks are vulnerable to various cyberattacks.
  • Effective intrusion detection systems are crucial for maintaining digital security.

Purpose of the Study:

  • To develop and validate an intelligent cybersecurity system for detecting network intrusions.
  • To evaluate the performance of machine learning and deep learning algorithms for intrusion detection.
  • To enhance the security of Internet of Things (IoT) devices and networks.

Main Methods:

  • Applied machine learning algorithms: Quantum Support Vector Machine (QSVM), k-nearest neighbor (KNN), Long Short-Term Memory (LSTM), and autoencoders.
  • Utilized correlation method for network feature selection, identifying nine key features.
  • Employed one-hot encoding for categorical feature conversion and validated using the KDD Cup database.

Main Results:

  • KNN and LSTM algorithms demonstrated superior performance in classifying network traffic as normal or attack.
  • Achieved high accuracy for binary classification (KNN: 98.55%, LSTM: 97.28%) and multiple classifications (KNN: 98.28%, LSTM: 97.07%).
  • Identified KNN and LSTM as suitable algorithms for building effective intrusion detection systems.

Conclusions:

  • The proposed intelligent cybersecurity system effectively detects network intrusions.
  • KNN and LSTM algorithms are highly effective for intrusion detection, offering robust classification performance.
  • The study highlights the potential of machine learning in securing IoT devices and networks against cyber threats.