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The artificial immune ecosystem : a scalable immune-inspired active classifier, an application to streaming time series analysis for network monitoring

Abstract : Since the early 1990s, immune-inspired algorithms have tried to adapt the properties of the biological immune system to various computer science problems, not only in computer security but also in optimization and classification. This work explores a different direction for artificial immune systems, focussing on the interaction between subsystems rather than the biological processes involved in each one. These patterns of interaction in turn create the properties expected from immune systems, namely their ability to detect anomalies, memorize their signature to react quickly upon secondary exposure, and remain tolerant to symbiotic foreign organisms such as the intestinal fauna. We refer to a set of interacting systems as an ecosystem, thus this new approach has called the Artificial Immune Ecosystem. We demonstrate this model in the context of a real-world problem where scalability and performance are essential: network monitoring. This entails time series analysis in real time with an expert in the loop, i.e. active learning instead of supervised learning.
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https://tel.archives-ouvertes.fr/tel-02316897
Contributor : Abes Star :  Contact
Submitted on : Tuesday, October 15, 2019 - 4:17:27 PM
Last modification on : Thursday, October 17, 2019 - 3:59:55 PM
Long-term archiving on: : Friday, January 17, 2020 - 8:25:07 AM

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Guigou_Fabio_2019_ED269.pdf
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  • HAL Id : tel-02316897, version 1

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Fabio Guigou. The artificial immune ecosystem : a scalable immune-inspired active classifier, an application to streaming time series analysis for network monitoring. Data Structures and Algorithms [cs.DS]. Université de Strasbourg, 2019. English. ⟨NNT : 2019STRAD007⟩. ⟨tel-02316897⟩

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