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pro vyhledávání: '"Soltani, Arian"'
Autor:
Masakuna, Jordan F., Nkashama, DJeff Kanda, Soltani, Arian, Frappier, Marc, Tardif, Pierre-Martin, Kabanza, Froduald
Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly undermines model performance. Most robust unsupervised anomaly detection
Externí odkaz:
http://arxiv.org/abs/2408.07718
Autor:
Nkashama, D'Jeff K., Félicien, Jordan Masakuna, Soltani, Arian, Verdier, Jean-Charles, Tardif, Pierre-Martin, Frappier, Marc, Kabanza, Froduald
Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learning patterns from data, they are vulnerable to data contamination -- th
Externí odkaz:
http://arxiv.org/abs/2407.08838
Autor:
Nkashama, D'Jeff Kanda, Soltani, Arian, Verdier, Jean-Charles, Frappier, Marc, Tardif, Pierre-Martin, Kabanza, Froduald
Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly detection-based in
Externí odkaz:
http://arxiv.org/abs/2207.03576