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Thomas Kapp, "Speeding-Up Snort with Traffic Classification," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), September 2021. (Advisor: Hossein Doroud; Referees: Falko Dressler and Thomas Sikora)


The development in modern network infrastructure is on a way that is exceeding. Every day more and more people get connected and the overall bandwidth and usage is growing. Furthermore, we tend to share more personal or even sensitive information in the internet and trust Network Intrusion Detection System (NIDS) like Snort to protect us from scammers. But, to cope with the rising load bearing on these systems, we are forced to improve their performance. In recent years, researches proved a significant speed-up by Load Distributor (LD) in parallel Snort systems. With this thesis, I continue this research by combining the performance advantages of signature and traffic distribution, which is done by classifying both according to their Application Layer Protocol (ALP). With this, I am able to improve the data throughput by the factor of 3 compared to a single Snort. I am also able to outperform the packet drop against traffic and rule distribution while requiring less processing resources than traffic distribution.

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Thomas Kapp

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    author = {Kapp, Thomas},
    title = {{Speeding-Up Snort with Traffic Classification}},
    advisor = {Doroud, Hossein},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {9},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2021},

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