Real-Time Detection of Fast Flux Service Networks

Abstract

Here we present the first empirical study of detecting and classifying fast flux service networks (FFSNs) in real time. FFSNs exploit a network of compromised machines (zombies) for illegal activities such as spam, phishing and malware delivery using DNS record manipulation techniques. Previous studies have focused on actively monitoring these activities over a large window (days, months) to detect such FFSNs and measure their footprint. In this paper, we present a Fast Flux Monitor (FFM) that can detect and classify a FFSN in the order of minutes using both active and passive DNS monitoring, which complements long term surveillance of FFSNs.