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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2502.19534 |
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| _version_ | 1866909514139172864 |
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| author | Pastoriza, Sam Yousfi, Iman Redino, Christopher Vucovich, Marc Rahman, Abdul Aguinaga, Sal Nandakumar, Dhruv |
| author_facet | Pastoriza, Sam Yousfi, Iman Redino, Christopher Vucovich, Marc Rahman, Abdul Aguinaga, Sal Nandakumar, Dhruv |
| contents | We propose a novel mechanism for real-time (human-in-the-loop) feedback focused on false positive reduction to enhance anomaly detection models. It was designed for the lightweight deployment of a behavioral network anomaly detection model. This methodology is easily integrable to similar domains that require a premium on throughput while maintaining high precision. In this paper, we introduce Retrieval Augmented Anomaly Detection, a novel method taking inspiration from Retrieval Augmented Generation. Human annotated examples are sent to a vector store, which can modify model outputs on the very next processed batch for model inference. To demonstrate the generalization of this technique, we benchmarked several different model architectures and multiple data modalities, including images, text, and graph-based data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining Pastoriza, Sam Yousfi, Iman Redino, Christopher Vucovich, Marc Rahman, Abdul Aguinaga, Sal Nandakumar, Dhruv Machine Learning Artificial Intelligence Cryptography and Security We propose a novel mechanism for real-time (human-in-the-loop) feedback focused on false positive reduction to enhance anomaly detection models. It was designed for the lightweight deployment of a behavioral network anomaly detection model. This methodology is easily integrable to similar domains that require a premium on throughput while maintaining high precision. In this paper, we introduce Retrieval Augmented Anomaly Detection, a novel method taking inspiration from Retrieval Augmented Generation. Human annotated examples are sent to a vector store, which can modify model outputs on the very next processed batch for model inference. To demonstrate the generalization of this technique, we benchmarked several different model architectures and multiple data modalities, including images, text, and graph-based data. |
| title | Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining |
| topic | Machine Learning Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2502.19534 |