Generally, Artificial Intelligence (AI) algorithms are unable to account for the logic of each decision they take during the course of arriving at a solution. This “black box” problem limits the usefulness of AI in military, medical, and financial security applications, among others, where the price for a mistake is great and the decision-maker must be able to monitor and understand each step along the process. In our research, we focus on the application of Explainable AI for log anomaly detection systems of a different kind. In particular, we use the Shapley value approach from cooperative game theory to explain the outcome or solution of two anomaly-detection algorithms: Decision tree and DeepLog. Both algorithms come from the machine learning-based log analysis toolkit for the automated anomaly detection “Loglizer”. The novelty of our research is that by using the Shapley value and special coding techniques we managed to evaluate or explain the contribution of both a single event and a grouped sequence of events of the Log for the purposes of anomaly detection. We explain how each event and sequence of events influences the solution, or the result, of an anomaly detection system.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com