Artificial Immune System Framework Research Articles

An artificial immune system for adaptive fault detection, diagnosis and recovery

The human immune system provides rich metaphors for adaptive pattern recognition. Fault detection and diagnosis in chemical processes is commonly formulated as a pattern recognition problem. However, conventional methods for fault diagnosis often do not have a mechanism to adapt and learn as the process changes over time. In this paper, we propose an Artificial Immune System (AIS) framework that endows learning to statistical process monitoring techniques such as Principal component analysis. The proposed AIS framework also provides a direct means to incorporate recovery actions after a failure has been detected and diagnosed. We demonstrate the efficacy of the proposed framework using a simulated binary distillation column case study.

A Machine Learning Evaluation of an Artificial Immune System

ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.