A Semi-Supervised Anomaly Network Traffic Detection Framework via Multimodal Traffic Information Fusion

Author:

Zheng Yu1ORCID,Lian Xinglin1ORCID,Dang Zhangxuan1ORCID,Peng Chunlei1ORCID,Yang Chao1ORCID,Ma Jianfeng1ORCID

Affiliation:

1. Xidian University, Xi'An, China

Publisher

ACM

Reference27 articles.

1. Nilesh A Ahuja , Ibrahima Ndiour , Trushant Kalyanpur , and Omesh Tickoo . 2019. Probabilistic modeling of deep features for out-of-distribution and adversarial detection. arXiv preprint arXiv:1909.11786 ( 2019 ). Nilesh A Ahuja, Ibrahima Ndiour, Trushant Kalyanpur, and Omesh Tickoo. 2019. Probabilistic modeling of deep features for out-of-distribution and adversarial detection. arXiv preprint arXiv:1909.11786 (2019).

2. Samet Akcay , Dick Ameln , Ashwin Vaidya , Barath Lakshmanan , Nilesh Ahuja , and Utku Genc . 2022 . Anomalib: A Deep Learning Library for Anomaly Detection. arXiv:2202.08341 [cs.CV] Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, and Utku Genc. 2022. Anomalib: A Deep Learning Library for Anomaly Detection. arXiv:2202.08341 [cs.CV]

3. Samet Akcay , Amir Atapour-Abarghouei , and Toby P Breckon . 2019 . Ganomaly: Semi-supervised anomaly detection via adversarial training. In ACCV. 622--637. Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon. 2019. Ganomaly: Semi-supervised anomaly detection via adversarial training. In ACCV. 622--637.

4. Issues and future directions in traffic classification

5. PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization

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