Szczegóły publikacji
Opis bibliograficzny
Active lifelong anomaly detection with experience replay / Kamil FABER, Roberto Corizzo, Bartłomiej ŚNIEŻYŃSKI, Nathalie Japkowicz // W: DSAA'2022 [Dokument elektroniczny] : 2022 IEEE 9th international conference on Data Science and Advanced Analytics : 13–16 October 2022, Shenzhen, China : proceedings / ed. by Joshua Zhexue Huang, [et al.]. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2022. — Dod. Print on Demand ISBN: 978-1-6654-7331-6. — e-ISBN: 978-1-6554-7330-9. — S. [1–10]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [10], Abstr. — Publikacja dostępna online od: 2023-02-08
Autorzy (4)
- AGHFaber Kamil
- Corizzo Roberto
- AGHŚnieżyński Bartłomiej
- Japkowicz Nathalie
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 145531 |
|---|---|
| Data dodania do BaDAP | 2023-03-20 |
| Tekst źródłowy | URL |
| DOI | 10.1109/DSAA54385.2022.10032405 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Data Science and Advanced Analytics 2022 |
Abstract
Anomaly detection tools present the potential to enhance defense policies and protection against different types of threats, supporting public safety and national security. Lifelong anomaly detection showcases new and challenging scenarios in which models are challenged to automatically adapt to changing conditions without forgetting past knowledge. However, the presence of anomalies in incoming data may significantly impact the robustness of models in such scenarios. Although active learning strategies could be an asset to increase model longevity and robustness, they have never been explored in this context. In this paper, we propose an active lifelong anomaly detection framework for class-incremental scenarios that supports any memory-based experience replay method, any query strategy, and any anomaly detection model. While experience replay allows models to consolidate past knowledge and simultaneously adapt to new knowledge, an active learning module reduces the number of anomalies memorized in the replay buffer. We propose two strategies that automatically identify and remove additional data points that are likely to be anomalies based on the oracle’s feedback. Our experiments on popular host-based and network-based intrusion detection datasets show that our framework can improve the anomaly detection performance of models under low labeling budget constraints.
