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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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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
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e-ISSN
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Number of pages
6
Pages from-to
2098-2103
Publisher name
IEEE Xplore
Place of publication
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Event location
Virtually
Event date
Dec 15, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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