In this work we propose a general approach for detecting distributed malicious activity in which individual attack sources each operate in a stealthy, low-profile manner. We base our approach on observing statistically significant changes in a parameter that summarizes aggregate activity, bracketing a distributed attack in time, and then determining which sources present during that interval appear to have coordinated their activity. We apply this approach to the problem of detecting stealthy distributed SSH bruteforcing activity, showing that we can model the process of legitimate users failing to authenticate using a beta-binomial distribution, which enables us to tune a detector that trades off an expected level of false positives versus time-to-detection. Using the detector we study the prevalence of distributed bruteforcing, finding dozens of instances in an extensive 8-year dataset collected from a site with several thousand SSH users. Many of the attacks---some of which last months---would be quite difficult to detect individually. While a number of the attacks reflect indiscriminant global probing, we also find attacks that targeted only the local site, as well as occasional attacks that succeeded.
['Mobin Javed', 'Vern Paxson']
Detecting stealthy, distributed SSH brute-forcing