Biblio

Recent work in OS fingerprinting has focused on overcoming random distortion in network and user features during Internet-scale SYN scans. These classification techniques work under an assumption that all parameters of the profiled network are known a-priori – the likelihood of packet loss, the popularity of each OS, the distribution of network delay, and the probability of user modification to each default TCP/IP header value. However, it is currently unclear how to obtain realistic versions of these parameters for the public Internet and/or customize them to a particular network being analyzed. To address this issue, we derive a non-parametric Expectation-Maximization (EM) estimator, which we call Faulds, for the unknown distributions involved in single-probe OS fingerprinting and demonstrate its significantly higher robustness to noise compared to methods in prior work. We apply Faulds to a new scan of 67M webservers and discuss its findings.

Maintaining and updating signature databases is a tedious task that normally requires a large amount of user effort. The problem becomes harder when features can be distorted by observation noise, which we call volatility. To address this issue, we propose algorithms and models to automatically generate signatures in the presence of noise, with a focus on stack fingerprinting, which is a research area that aims to discover the operating system (OS) of remote hosts using TCP/IP packets. Armed with this framework, we construct a database with 420 network stacks, label the signatures, develop a robust classifier for this database, and fingerprint 66M visible webservers on the Internet.