Enhancing DeCrypto: Finding Cryptocurrency Miners Based on Periodic Behavior

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F23%3A10133612" target="_blank" >RIV/63839172:_____/23:10133612 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/23:00369773

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10327904" target="_blank" >https://ieeexplore.ieee.org/document/10327904</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/CNSM59352.2023.10327904" target="_blank" >10.23919/CNSM59352.2023.10327904</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing DeCrypto: Finding Cryptocurrency Miners Based on Periodic Behavior

Popis výsledku v původním jazyce

While the popularity of cryptocurrencies and the whole industry&apos;s value are rising, the number of threat actors who use illegal &quot;coin miner mal ware&quot; is increasing as well. The threat actors commonly use computational resources of companies, research and educational institutions, or end users. In this paper, we analyzed the long-term periodic behavior of the cryptocurrency miners communicating in computer networks. We propose a novel method for cryptominers detection using specially designed periodicity features. The detection algorithm is based on the mathematical detection of periodic Flow time series (FTS) and feature mining. Altogether with the Machine Learning technique, the resulting system achieves high-precision performance. Furthermore, our approach enhances a flow-based cryptominers detection system DeCrypto to further improve its reliability and feasibility for high-speed networks.

Název v anglickém jazyce

Enhancing DeCrypto: Finding Cryptocurrency Miners Based on Periodic Behavior

Popis výsledku anglicky

While the popularity of cryptocurrencies and the whole industry&apos;s value are rising, the number of threat actors who use illegal &quot;coin miner mal ware&quot; is increasing as well. The threat actors commonly use computational resources of companies, research and educational institutions, or end users. In this paper, we analyzed the long-term periodic behavior of the cryptocurrency miners communicating in computer networks. We propose a novel method for cryptominers detection using specially designed periodicity features. The detection algorithm is based on the mathematical detection of periodic Flow time series (FTS) and feature mining. Altogether with the Machine Learning technique, the resulting system achieves high-precision performance. Furthermore, our approach enhances a flow-based cryptominers detection system DeCrypto to further improve its reliability and feasibility for high-speed networks.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/VJ02010024" target="_blank" >VJ02010024: Analýza šifrovaného provozu pomocí síťových toků</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 19th International Conference on Network and Service Management (CNSM)

ISBN

978-3-903176-59-1

ISSN

2165-963X

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway , USA

Místo konání akce

Niagara Falls, Kanada

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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