An alarm correlation algorithm based on similarity distance and deep network

Abstract

Currently, a few alarm correlation algorithms are based on a framework involving frequency and support-confidence. These algorithms often fail to address text data in alarm records and cannot handle high-dimensional data. This paper proposes an algorithm based on the similarity distance and deep networks. The algorithm first translates text data in alarms to real number vectors; second, it reconstructs the input, obtains the alarm features through a deep network system and performs dimension reduction; and finally, it presents the alarm distribution visually and helps the administrator determine the new fault. Experimental results demonstrate that it cannot only mine the correlation among alarms but also determine the new fault quickly by comparing the graphs of the alarm distribution.