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Advancing Network Security with AI: SVM-Based Deep Learning for Intrusion Detection.

Khadija M Abuali1, Liyth Nissirat1, Aida Al-Samawi1

  • 1Department of Computer Networks, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

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Summary

This study introduces a novel intrusion detection system (IDS) using support vector machine (SVM) deep learning. The system achieves 100% accuracy in identifying network intrusions on social media.

Keywords:
CIC-IDS2018deep learningintrusion detection systemmulticlass classificationsupport vector machines

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Businesses face increasing cyber threats due to social media growth and internet accessibility.
  • Intrusion detection systems (IDSs) are crucial for network security, analyzing traffic to identify malicious activities.
  • Evolving attack vectors and network complexity necessitate advanced IDS solutions, including AI-driven methods.

Purpose of the Study:

  • To propose a support vector machine (SVM)-based deep learning system for intrusion detection on social media networks.
  • To classify server-extracted data for identifying intrusion incidents.
  • To evaluate the system's effectiveness using the CSE-CIC-IDS 2018 dataset.

Main Methods:

  • A deep learning system utilizing Support Vector Machine (SVM) was developed.
  • The CSE-CIC-IDS 2018 dataset was employed for system evaluation.
  • Data preprocessing techniques were applied to the dataset before training the model on 100,000 instances.

Main Results:

  • The proposed SVM-based deep learning IDS achieved perfect scores across key metrics.
  • Accuracy, true-positive recall, precision, specificity, and F-score were all recorded at 100%.
  • False-positive recall was 0%, indicating no misclassification of normal traffic as malicious.

Conclusions:

  • The developed deep learning IDS demonstrates exceptional performance in detecting social media intrusions.
  • The SVM-based approach effectively classifies network traffic, providing robust security.
  • This system offers a highly accurate and reliable solution for modern cybersecurity challenges.