  In this paper we propose a novel approach to identify anomalies in DNS
traffic. The traffic time-points data is transformed to a string, which is used
by new fast appproximate string matching algorithm to detect anomalies. Our
approach is generic in its nature and allows fast adaptation to different types
of traffic. We evaluate the approach on a large public dataset of DNS traffic
based on 10 days, discovering more than order of magnitude DNS attacks in
comparison to auto-regression as a baseline. Moreover, the additional
comparison has been made including other common regressors such as Linear
Regression, Lasso, Random Forest and KNN, all of them showing the superiority
of our approach.
