Thesis: The Artificial Immune Ecosystem: a scalable immune-inspired active classifier

PhD defense: Fabio GUIGOU

Title: The Artificial Immune Ecosystem: a scalable immune-inspired active classifier

Team : CSTB

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 is 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.

This thesis was directed by Pierre Collet (Professor, University of Strasbourg) and Pierre Parrend (Professeur d'enseignement supérieur, ECAM Strasbourg-Europe, HDR). Its jury is also composed of Dominique Pastor (Professor, IMT Atlantique - rewiever), Juan J. Merelo (Professeur, University of Granada - rewiever) and Véronique Legrand (Professor, National Conservatory of Arts and Crafts - other member).

Keywords: artificial immune system, network monitoring, time series analysis, active learning

The defense will be held in English on Tuesday, 18th June at 9:00am in the amphitheater of the ECAM Strasbourg-Europe (Schiltigheim).