Machine Learning Optimization for Enhanced OS Fingerprinting

Jae Sung Kim, Spencer Ekeroth, Jeremy Neale
10/8/2026
cs.LG

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Abstract

Operating System (OS) Fingerprinting is a technique that can be used to identify a network's operating systems by evaluating network traffic in the form of TCP/IP packets. This research will explore the effectiveness of passively identifying operating systems on the CIC-IDS2017 dataset, a collection of over 47 gigabytes of pcap files with their corresponding operating systems. This research also proposes a new command line interface, OsirisML, which uses nPrint to preprocess the data into tabular data and XGBoost to apply ML to the data to generate, retrain, and test ML models. When packets are split randomly between training and testing, OsirisML models reach an accuracy of 97.66% on a down-sampled subset of the Friday capture and 84.69% on the entire capture. On the entire Monday capture, which contains no attacks, OsirisML reaches an accuracy of 73.83% and an F-1 score of 79.38%.

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Cite this paper

@article{kim2026machine,
  title  = {Machine Learning Optimization for Enhanced OS Fingerprinting},
  author = {Jae Sung Kim and Spencer Ekeroth and Jeremy Neale},
  year   = {2026},
  eprint = {2610.11133},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.11133v1}
}

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