Towards Situational Awareness of Large-Scale Botnet Probing Events

Botnets dominate today's attack landscape. In this work, we investigate ways to analyze collections of malicious probing traffic in order to understand the significance of large-scale “botnet probes.” In such events, an entire collection of remote hosts together probes the address space monitored by a sensor in some sort of coordinated fashion. Our goal is to develop methodologies by which sites receiving such probes can infer-using purely local observation-information about the probing activity: What scanning strategies does the probing employ? Is this an attack that specifically targets the site, or is the site only incidentally probed as part of a larger, indiscriminant attack? Our analysis draws upon extensive honeynet data to explore the prevalence of different types of scanning, including properties, such as trend, uniformity, coordination, and darknet avoidance. In addition, we design schemes to extrapolate the global properties of scanning events (e.g., total population and target scope) as inferred from the limited local view of a honeynet. Cross-validating with data from DShield shows that our inferences exhibit promising accuracy.