Artificial Immune Ecosystems: the role of expert-based learning in artificial cognition
- 10.2991/jrnal.2018.4.4.10How to use a DOI?
- Artificial Immune Systems, Cybersecurity, Immunity, Computational Ecosystem, Anomaly detection
The rapid evolution of IT ecosystems significantly challenges the security models our infrastructures rely on. Beyond the old dichotomy between open and closed systems, it is now necessary to handle securely the interaction between heterogeneous devices building dynamic ecosystems. To this regard, bio-inspired approaches provide a rich set of conceptual tools, but they have failed to lay the basis for robust and efficient solutions. Our research effort intends to revisit the contribution of artificial immune system research to bring immune properties: security, resilience, distribution, memory, into IT infrastructures. Artificial immune ecosystems support a comprehensive model for anomaly detection and characterization, but their cognitive capacity are limited by the state of the art in machine learning and the rapid evolution of cybersecurity threats so far. We therefore propose to enrich the cognitive process with expert-based learning for reinforcement, classification and investigation. Application to system supervision using system logs and supervision time series confirms the relevance and performance of this model.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Pierre Parrend AU - Fabio Guigou AU - Julio Navarro AU - Aline Deruyver AU - Pierre Collet PY - 2018 DA - 2018/03/31 TI - Artificial Immune Ecosystems: the role of expert-based learning in artificial cognition JO - Journal of Robotics, Networking and Artificial Life SP - 303 EP - 307 VL - 4 IS - 4 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.2018.4.4.10 DO - 10.2991/jrnal.2018.4.4.10 ID - Parrend2018 ER -