Detecting stealthy spreaders using online outdegree histograms

We consider the problem of detecting the presence of a sufficiently large number of hosts that connect to more than a certain number of unique destinations within a given time window, over high-speed networks. We call such hosts stealthy spreaders. In practice, stealthy spreaders can be symptomatic of botnet scans or moderate worm propagation. Previous techniques have focused on detecting sources with an extremely large outdegree. However, such techniques will fail to detect spreaders such as bot scans in which each scanning host will scan only a moderate, fixed number of destinations. In contrast, our scheme maintains a small, fixed size memory usage, and is still able to detect stealthy spreader scenarios by approximating outdegree histograms from continuous traffic. To the best of our knowledge, we are the first to study the efficient outdegree histogram estimation and stealthy spreader detection problems. Evaluation based on real Internet traffic and botnet scan events show that our scheme is highly accurate and can operate online.