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Falko Dressler, "Intrusion Detection in High-Speed Networks: From Packets to Flows and Back," Invited Lecture, Department of Computer Science, TU Dresden, Dresden, Germany, July 01, 2019.


In this talk we revisit the problem of network intrusion detection. Many popular systems like Snort rely on complex rule sets and use state machines according to these rules for processing packets observed in the network. Matches found represent true positive events, which can be (parts of) attacks. The key issue is processing performance. Even using high-end CPUs, the data rate is rather limited. One possible way out of this dilemma is to rely on anomalies in the network by learning (online or offline) what normal behavior is and then to report deviations. Here, the system does not need to look into each packet individually and it is sufficient to work on statistical properties of entire network flows. The advantage is the very high data rates supported; the downside is a possibly high number of false positives and false negatives, i.e., the number of mis-detected and the number of missed attacks. Flow monitoring has been investigates in-depth in the last years and current solutions even support to add selected parts of payload to flows. Thus, we can turn back and use pattern matching based on rules but hopefully maintaining the very high processing speed. We explore these trends, highlight relevant research questions, and, study solutions to (parts of) these issues.

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Falko Dressler

BibTeX reference

    author = {Dressler, Falko},
    title = {{Intrusion Detection in High-Speed Networks: From Packets to Flows and Back}},
    howpublished = {Invited Lecture},
    publisher = {Department of Computer Science, TU Dresden},
    location = {Dresden, Germany},
    day = {01},
    month = {07},
    year = {2019},

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Last modified: 2024-05-29