Sampling High Throughput Data for Anomaly Detection of Data-Base Activity
Abstract
Data leakage and theft from databases is a dangerous threat to organizations. Data Security and Data Privacy protection systems (DSDP) monitor data access and usage to identify leakage or suspicious activities that should be investigated. Because of the high velocity nature of database systems, such systems audit only a portion of the vast number of transactions that take place. Anomalies are investigated by a Security Officer (SO) in order to choose the proper response. In this paper we investigate the effect of sampling methods based on the risk the transaction poses and propose a new method for "combined sampling" for capturing a more varied sample.
Cite
@article{arxiv.1708.04278,
title = {Sampling High Throughput Data for Anomaly Detection of Data-Base Activity},
author = {Hagit Grushka-Cohen and Oded Sofer and Ofer Biller and Michael Dymshits and Lior Rokach and Bracha Shapira},
journal= {arXiv preprint arXiv:1708.04278},
year = {2017}
}
Comments
Proceedings of the 11th Pre-ICIS Workshop on Information Security and Privacy, Dublin, Ireland December 10, 2016