Weakly Supervised Anomaly Detection via Knowledge-Data Alignment Article Swipe
Haihong Zhao
,
Chenyi Zi
,
Yang Liu
,
Chen Zhang
,
Yan Zhou
,
Jia Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.03785
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.03785
Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Most methods, which rely on unsupervised learning, are hard to reach satisfactory detection accuracy due to the lack of labels. Weakly Supervised Anomaly Detection (WSAD) has been introduced with a limited number of labeled anomaly samples to enhance model performance. Nevertheless, it is still challenging for models, trained on an inadequate amount of labeled data, to generalize to unseen anomalies. In this paper, we introduce a novel framework Knowledge-Data Alignment (KDAlign) to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data. Specifically, we transpose these rules into the knowledge space and subsequently recast the incorporation of knowledge as the alignment of knowledge and data. To facilitate this alignment, we employ the Optimal Transport (OT) technique. We then incorporate the OT distance as an additional loss term to the original objective function of WSAD methodologies. Comprehensive experimental results on five real-world datasets demonstrate that our proposed KDAlign framework markedly surpasses its state-of-the-art counterparts, achieving superior performance across various anomaly types.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.03785
- https://arxiv.org/pdf/2402.03785
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391631781
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391631781Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.03785Digital Object Identifier
- Title
-
Weakly Supervised Anomaly Detection via Knowledge-Data AlignmentWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-06Full publication date if available
- Authors
-
Haihong Zhao, Chenyi Zi, Yang Liu, Chen Zhang, Yan Zhou, Jia LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.03785Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2402.03785Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2402.03785Direct OA link when available
- Concepts
-
Anomaly detection, Anomaly (physics), Computer science, Artificial intelligence, Pattern recognition (psychology), Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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