Faulds: A Non-Parametric Iterative Classifier for Internet-Wide OS Fingerprinting
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 popu...
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Veröffentlicht in: | IEEE/ACM transactions on networking 2021-10, Vol.29 (5), p.2339-2352 |
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Zusammenfassung: | 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. |
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ISSN: | 1063-6692 1558-2566 |
DOI: | 10.1109/TNET.2021.3088333 |