Details

Autor(en) / Beteiligte

• Peng, Hongyi
• Sachidananda, Vinay
• Lim, Teng Joon
• Patil, Rajendra
• Liu, Mingchang
• Muneeswaran, Sivaanandh
• Gurusamy, Mohan

Titel

ODDITY: An Ensemble Framework Leverages Contrastive Representation Learning for Superior Anomaly Detection

Ist Teil von

• Information and Communications Security, p.417-437

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Ensemble approaches are promising for anomaly detection due to the heterogeneity of network traffic. However, existing ensemble approaches lack applicability and efficiency. We propose ODDITY, a new end-to-end data-driven ensemble framework. ODDITY use Diverse Autoencoders trained on a pre-clustered subset with contrastive representation learning to encourage base-leaners to give distinct predictions. Then, ODDITY combines the extracted features with a supervised gradient boosting meta-learner. Experiments using benchmarking and real-world network traffic datasets demonstrate that ODDITY is superior in terms of efficiency and precision. ODDITY averages 0.8350 AUPRC on benchmarking datasets (10% better than traditional machine learning algorithms and 6% better than the state-of-the-art semi-supervised ensemble method). ODDITY also outperforms the state-of-the-art on real-world datasets regarding better detection accuracy and speed. Moreover, ODDITY is more resilient to evasion attacks and has a promising potential for unsupervised anomaly detection.