Using Neural Networks to improve classical Operating System Fingerprinting techniques

Details Using Neural Networks to improve classical Operating System Fingerprinting techniques Using Neural Networks to improve classical Operating System Fingerprinting techniques

2010-06-09

arxiv.org/abs/1006.1918v1

Electronic Journal of SADIO, Vol. 8, no. 1, pp. 35-47 (2008)

Computer Science

Cryptography and Security

Scientific paper

We present remote Operating System detection as an inference problem: given a set of observations (the target host responses to a set of tests), we want to infer the OS type which most probably generated these observations. Classical techniques used to perform this analysis present several limitations. To improve the analysis, we have developed tools using neural networks and Statistics tools. We present two working modules: one which uses DCE-RPC endpoints to distinguish Windows versions, and another which uses Nmap signatures to distinguish different version of Windows, Linux, Solaris, OpenBSD, FreeBSD and NetBSD systems. We explain the details of the topology and inner workings of the neural networks used, and the fine tuning of their parameters. Finally we show positive experimental results.

Affiliated with

Also associated with

No associations

Rating

Using Neural Networks to improve classical Operating System Fingerprinting techniques does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Using Neural Networks to improve classical Operating System Fingerprinting techniques, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Using Neural Networks to improve classical Operating System Fingerprinting techniques will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-490326