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• W2016475179 abstract "The classification of Encrypted Traffic, namely Secure Shell (SSH), on the fly from network TCP traffic represents a particularly challenging application domain for machine learning. Solutions should ideally be both simple - therefore efficient to deploy - and accurate. Recent advances to teambased Genetic Programming provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviors, in effect providing further insight into the problem domain and increasing the throughput of solutions. Thus, in this work we have investigated the identification of SSH encrypted traffic based on packet header features without using IP addresses, port numbers and payload data. Evaluation of C4.5 and AdaBoost - representing current best practice - against the Symbiotic Bid-based (SBB) paradigm of team-based Genetic Programming (GP) under data sets common and independent from the training condition indicates that SBB based GP solutions are capable of providing simpler solutions without sacrificing accuracy." @default.
• W2016475179 created "2016-06-24" @default.
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• W2016475179 date "2009-07-08" @default.
• W2016475179 modified "2023-09-25" @default.
• W2016475179 title "Classifying SSH encrypted traffic with minimum packet header features using genetic programming" @default.
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• W2016475179 doi "https://doi.org/10.1145/1570256.1570358" @default.
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