כתבה
arXiv cs.LG ·
אופטימיזציה של Machine Learning לצורך זיהוי OS
Machine Learning Optimization for Enhanced OS Fingerprinting
במחקר זה נבחן אופטימיזציה של Machine Learning לצורך זיהוי OS. המחקר כולל ניסויים על קבצי pcap ושימוש ב- OsirisML, כלי פקודה חדש.
תקציר מקורי באנגליתarXiv:2610.11133v1 Announce Type: new 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
קרא במקור המקורי
arxiv.org
פתח כתבה מקורית