Abstract
Remote OS fingerprinting is valuable in areas such as network security, Internet modeling, and end-to-end application design, etc. While current rule-based tools fail to detect the OS of remote host with high accuracy, for users may modify their TCP/IP parameters or employ stack “scrubbers”. In this paper, a BP neural network based classifier is proposed for accurately fingerprinting the OS of remote host. To avoid the shortages of traditional BP algorithm, the classifier is also enforced with Levenberg-Marquardt algorithm. Experimental results on packet traces collected at an access link of a website show that, rule-based tools can’t identify as many as 10.6% of the hosts. While the BP neural network based classifier is far more accurate, it can successfully identify about 97.8% hosts in the experiment.
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Li, W., Zhang, D., Yang, J. (2005). Remote OS Fingerprinting Using BP Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_59
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DOI: https://doi.org/10.1007/11427469_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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