Multilayer Framework for Botnet Detection Using Machine Learning Algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017988" target="_blank" >RIV/62690094:18450/21:50017988 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/abstract/document/9359784" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9359784</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2021.3060778" target="_blank" >10.1109/ACCESS.2021.3060778</a>

Result language

angličtina

Original language name

Multilayer Framework for Botnet Detection Using Machine Learning Algorithms

Original language description

A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. The traditional method of detecting botnets commonly used signature-based analysis unable to detect unseen botnets. The behavior-based analysis seems like a promising solution to the current trends of botnets that keep evolving. This paper proposes a multilayer framework for botnet detection using machine learning algorithms that consist of a filtering module and classification module to detect the botnet&apos;s command and control server. We highlighted several criteria for our framework, such as it must be structure-independent, protocol-independent, and able to detect botnet in encapsulated technique. We used behavior-based analysis through flow-based features that analyzed the packet header by aggregating it to a 1-s time. This type of analysis enables detection if the packet is encapsulated, such as using a VPN tunnel. We also extend the experiment using different time intervals, but a 1-s time interval shows the most impressive results. The result shows that our botnet detection method can detect up to 92% of the f-score, and the lowest false-negative rate was 1.5%.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

February

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

48753-48768

UT code for WoS article

000637188400001

EID of the result in the Scopus database

2-s2.0-85101756348