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August 30, 2022World Journal of Advanced Research and Reviews

Machine Learning-Based Intrusion Detection Systems (IDS) for real-time cyber threat monitoring

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Authors

SZSufia ZareenKHKhandaker M. Anwar HossainMMMohd Abdullah Al Mamun

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Overview

Observational analysis reveals machine learning enhances accuracy in intrusion detection systems, suggesting improved cybersecurity measures.

Key Points

  • Gradient Boosting and Random Forest achieved perfect accuracy in detecting cyber threats, enhancing network security.
  • The study found Random Forest to be the most reliable method for real-time intrusion detection with exemplary performance.
  • Machine learning models effectively classify anomalies within network traffic, indicating their strength against cyberattacks.
  • The results highlight the need for scalable intrusion detection systems to adapt to evolving cyber threats, ensuring robust protection.

Cite This Study

Zareen et al. (2022) studied this question.

synapsesocial.com/papers/68af66dfad7bf08b1eae6136https://doi.org/10.30574/wjarr.2022.15.2.0706
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Network Intrusion Detection Using Machine Learning2025
  2. 2Intrusion Detection System Using Machine Learning2025
  3. 3Comparing Machine Learning Algorithms for Intrusion Detection Systems2025
  4. 4Designing IDS to Analyze Malicious Attacks using Machine Learning2024
  5. 5Empirical Analysis of Machine Learning Models towards Adaptive Network Intrusion Detection Systems2022 · 1 citations