Ting-Fang Yen

Beehive: large-scale log analysis for detecting suspicious activity in enterprise networks

As more and more Internet-based attacks arise, organizations are responding by deploying an assortment of security products that generate situational intelligence in the form of logs. These logs often contain high volumes of interesting and useful information about activities in the network, and are among the first data sources that information security specialists consult when they suspect that an attack has taken place. However, security products often come from a patchwork of vendors, and are inconsistently installed and administered. They generate logs whose formats differ widely and that are often incomplete, mutually contradictory, and very large in volume. Hence, although this collected information is useful, it is often dirty. We present a novel system, Beehive, that attacks the problem of automatically mining and extracting knowledge from the dirty log data produced by a wide variety of security products in a large enterprise. We improve on signature-based approaches to detecting security incidents and instead identify suspicious host behaviors that Beehive reports as potential security incidents. These incidents can then be further analyzed by incident response teams to determine whether a policy violation or attack has occurred. We have evaluated Beehive on the log data collected in a large enterprise, EMC, over a period of two weeks. We compare the incidents identified by Beehive against enterprise Security Operations Center reports, antivirus software alerts, and feedback from enterprise security specialists. We show that Beehive is able to identify malicious events and policy violations which would otherwise go undetected.

Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data

Recent years have seen the rise of sophisticated attacks including advanced persistent threats (APT) which pose severe risks to organizations and governments. Additionally, new malware strains appear at a higher rate than ever before. Since many of these malware evade existing security products, traditional defenses deployed by enterprises today often fail at detecting infections at an early stage. We address the problem of detecting early-stage APT infection by proposing a new framework based on belief propagation inspired from graph theory. We demonstrate that our techniques perform well on two large datasets. We achieve high accuracy on two months of DNS logs released by Los Alamos National Lab (LANL), which include APT infection attacks simulated by LANL domain experts. We also apply our algorithms to 38TB of web proxy logs collected at the border of a large enterprise and identify hundreds of malicious domains overlooked by state-of-the-art security products.