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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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TTANAD: Test-Time Augmentation for Network Anomaly Detection.

Seffi Cohen1, Niv Goldshlager1, Bracha Shapira1

  • 1Software and Information Systems Engineering, Ben-Gurion University, Beer Sheva P.O. Box 653, Israel.

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Summary
This summary is machine-generated.

This study introduces Test-Time Augmentation for Network Anomaly Detection (TTANAD) to improve machine learning-based intrusion detection. TTANAD enhances network traffic analysis, significantly boosting detection accuracy across various datasets and algorithms.

Keywords:
NIDSTTAanomaly detectiontime series

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Machine learning-based Network Intrusion Detection Systems (NIDS) are crucial for network protection.
  • Advanced attacks increasingly evade traditional NIDS by mimicking legitimate traffic.

Purpose of the Study:

  • To introduce a novel data-centric approach, Test-Time Augmentation for Network Anomaly Detection (TTANAD).
  • To enhance the performance of NIDS by improving data representation during inference.

Main Methods:

  • TTANAD utilizes temporal test-time augmentation on network traffic data.
  • This method generates diverse viewpoints of traffic data for anomaly detection algorithms.
  • It is designed to be compatible with various existing anomaly detection algorithms.

Main Results:

  • TTANAD demonstrated superior performance compared to baseline methods across all benchmark datasets.
  • The proposed method consistently improved detection accuracy, as measured by the Area Under the ROC Curve (AUC) metric.
  • Effectiveness was validated across multiple anomaly detection algorithms.

Conclusions:

  • TTANAD offers a significant advancement in network anomaly detection by focusing on data augmentation.
  • The approach enhances the robustness and accuracy of NIDS against sophisticated network attacks.
  • TTANAD presents a versatile and effective strategy for improving network security.