*Learning detector of malicious network communication from data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00223250" target="_blank" >RIV/68407700:21230/14:00223250 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
*Learning detector of malicious network communication from data
Original language description
*This report summarizes our effort which was dedicated to the problem of learning detectors of a malicious network communication from data. We concentrated on two problems. First, improving the currently used manually created layer-two (L2) detector by using the Support Vector Machine detector learned from examples. We showed that L2-detectors learned automatically from examples have slightly yet consistently better accuracy than the manually created one. Second, we concentrated on designing tools helping to discover new instances of malicious communication in a large pool of unannotated data. We developed a method producing a ranked list of unannotated examples highly suspected from being malicious. The method helped to identify new instances of malicious communication in records of real network traffic.
Czech name
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Czech description
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Publication year
2014
Confidentiality
C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.

Number of pages
10
Place of publication
Praha
Publisher/client name
Center for Machine Perception, K13133 FEE Czech Technical University
Version
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