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- W764677550 abstract "Intrusion detection systems (IDS) analyse network traffic data with the goal to reveal malicious activities and incidents. A general problem with learning within this domain is a lack of relevant ground truth data, i.e. real attacks, capturing malicious behaviors in their full variety. Most of existing solutions thus, up to a certain level, rely on rules designed by network domain experts. Although there are advantages to the use of rules, they lack the basic ability of adapting to traffic data. As a result, we propose an ensemble tree bagging classifier, capable of learning from an extremely small number of true attack representatives, and demonstrate that, incorporating a general background traffic, we are able to generalize from those few representatives to achieve competitive results to the expert designed rules used in existing IDS Camnep." @default.
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- W764677550 date "2015-01-01" @default.
- W764677550 modified "2023-10-16" @default.
- W764677550 title "Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic" @default.
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- W764677550 doi "https://doi.org/10.1007/978-3-319-20034-7_9" @default.
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