DOI

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.

Язык оригиналаанглийский
Название основной публикацииMachine Learning and Artificial Intelligence
Подзаголовок основной публикацииProceedings of MLIS 2020
РедакторыAntonio J. Tallon-Ballesteros, Chi-Hua Chen
ИздательIOS Press
Страницы152-164
ISBN (электронное издание)9781643681368
DOI
СостояниеОпубликовано - 2 дек 2020
Событие2020 International Conference on Machine Learning and Intelligent Systems, MLIS 2020 - Virtual, Online, Республика Корея
Продолжительность: 25 окт 202028 окт 2020

Серия публикаций

НазваниеFrontiers in Artificial Intelligence and Applications
Том332
ISSN (печатное издание)0922-6389

конференция

конференция2020 International Conference on Machine Learning and Intelligent Systems, MLIS 2020
Страна/TерриторияРеспублика Корея
ГородVirtual, Online
Период25/10/2028/10/20

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