Gang Long, Zhaoxin Zhang*
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 327-343, 2024, DOI:10.32604/cmc.2024.054558
- 15 October 2024
Abstract Network anomaly detection plays a vital role in safeguarding network security. However, the existing network anomaly detection task is typically based on the one-class zero-positive scenario. This approach is susceptible to overfitting during the training process due to discrepancies in data distribution between the training set and the test set. This phenomenon is known as prediction drift. Additionally, the rarity of anomaly data, often masked by normal data, further complicates network anomaly detection. To address these challenges, we propose the PUNet network, which ingeniously combines the strengths of traditional machine learning and deep learning techniques… More >
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