Utilising Flow Aggregation to Classify Benign Imitating Attacks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00349700" target="_blank" >RIV/68407700:21230/21:00349700 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/s21051761" target="_blank" >https://doi.org/10.3390/s21051761</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21051761" target="_blank" >10.3390/s21051761</a>

Result language

angličtina

Original language name

Utilising Flow Aggregation to Classify Benign Imitating Attacks

Original language description

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

Czech name

—

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Sensors - Open Access Journal

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

21

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

1-17

UT code for WoS article

000628600700001

EID of the result in the Scopus database

2-s2.0-85101932116