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September 10, 2025International Scientific Journal of Engineering and Management

Network Intrusion Detection Using Machine Learning

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Authors

BABOBBADI ADARSHCVCH. VASUNDHARA

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Overview

Observational analysis improves intrusion detection in network security, highlighting the role of machine learning.

Key Points

  • Integrating machine learning enhances the accuracy of intrusion detection systems against evolving cybersecurity threats.
  • Machine learning-based IDS can learn from data patterns, which helps reduce false alarms in network monitoring.
  • The implementation of advanced machine learning techniques, like random forest, aids in identifying novel cyberattacks effectively.
  • Recent advancements in data preprocessing techniques have significantly improved the performance of intrusion detection systems.

Cite This Study

ADARSH et al. (2025) studied this question.

synapsesocial.com/papers/68c1b36054b1d3bfb60ea4a6https://doi.org/10.55041/isjem04803
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Also Consider

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

  1. 1A Review of Machine Learning-Based Intrusion Detection Systems in Computer Networks2026
  2. 2Network Intrusion Detection System using Machine Learning2024
  3. 3Intrusion Detection System Using Machine Learning2025
  4. 4A review of machine and deep learning techniques for network intrusion detection2026 · 1 citations
  5. 5Empirical Analysis of Machine Learning Models towards Adaptive Network Intrusion Detection Systems2022 · 1 citations