ATENA: An Autonomous System for Data Exploration Based on Deep Reinforcement Learning

Ori Bar El, Tova Milo, Amit Somech

Abstract: Exploratory Data Analysis (EDA), is an important yet challenging task, that requires profound analytical skills and familiarity with the data domain. While Deep Reinforcement Learning (DRL) is nowadays used to solve AI challenges previously considered to be intractable, to our knowledge such solutions have not yet been applied to EDA. In this work we present ATENA, an autonomous system capable of exploring a given dataset by executing a meaningfulsequence of EDA operations. ATENA uses a novel DRL architecture, and learns to perform EDA operations by independently interacting with the dataset, without any training data or human assistance. We demonstrate ATENA in the context ofcyber security log analysis, where the audience is invited to partake in a data exploration challenge: explore real-life network logs, assisted by ATENA, in order to reveal underlying security attacks hidden in the data.