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Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms

  • Unique Paper ID: 161987
  • Volume: 10
  • Issue: 7
  • PageNo: 154-160
  • Abstract:
  • This project focuses on advanced machine learning for detecting intrusions in imbalanced network traffic. Despite challenges posed by data imbalance, the study employs cutting-edge algorithms and diverse preprocessing to glean insights from normal and malicious patterns. Findings highlight machine learning's potential in managing imbalanced network traffic, emphasizing tailored preprocessing and algorithm selection. Ultimately, the project advances intrusion detection by showcasing machine learning's role in enhancing security through swift threat identification and mitigation. In conclusion, this research underscores the pivotal role of machine learning in addressing imbalanced network scenarios, paving the way for a safer digital landscape.
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  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 7
  • PageNo: 154-160

Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning algorithms

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