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Threat Hunting as a Similarity Search Problem on Multi-positive and Unlabeled Data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354915" target="_blank" >RIV/68407700:21230/21:00354915 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/BigData52589.2021.9671958" target="_blank" >https://doi.org/10.1109/BigData52589.2021.9671958</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/BigData52589.2021.9671958" target="_blank" >10.1109/BigData52589.2021.9671958</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Threat Hunting as a Similarity Search Problem on Multi-positive and Unlabeled Data

  • Original language description

    We present a new similarity search method (called Random Separations) that helps threat analysts with identification of unknown variants of known malware in network traffic. The method assumes that for each hunted malware family, few samples of network communication are available to analysts (multi-positive) and others are hidden in abundant (unlabeled) network data. We demonstrate the method on large-scale real-world data, where it outperforms the unsupervised approach (Isolation Forest and Lightweight Online Detector of Anomalies), the supervised approach (Random Forest) and the traditional similarity search algorithm (kNN). The evaluation involves eight high-risk malware families under various known/unknown ratios.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 2021 IEEE International Conference on Big Data

  • ISBN

    978-1-6654-3902-2

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    2098-2103

  • Publisher name

    IEEE Xplore

  • Place of publication

  • Event location

    Virtually

  • Event date

    Dec 15, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article