GRU-based deep learning approach for network intrusion alert prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F21%3A10133392" target="_blank" >RIV/63839172:_____/21:10133392 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0167739X21003861" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0167739X21003861</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.future.2021.09.040" target="_blank" >10.1016/j.future.2021.09.040</a>

Result language

angličtina

Original language name

GRU-based deep learning approach for network intrusion alert prediction

Original language description

The exponential growth in the number of cyber attacks in the recent past has necessitated active research on network intrusion detection, prediction and mitigation systems. While there are numerous solutions available for intrusion detection, the prediction of future network intrusions still remains an open research problem. Existing approaches employ statistical and/or shallow machine learning methods for the task, and therefore suffer from the need for feature selection and engineering. This paper presents a deep learning based approach for prediction of network intrusion alerts. A Gated Recurrent Unit (GRU) based deep learning model is proposed which is shown to be capable of learning dependencies in security alert sequences, and to output likely future alerts given a past history of alerts from an attacking source. The performance of the model is evaluated on intrusion alert sequences obtained from the Warden alert sharing platform.

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

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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)

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2021

Confidentiality

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

Name of the periodical

Future Generation Computer Systems

ISSN

0167-739X

e-ISSN

—

Volume of the periodical

Neuveden

Issue of the periodical within the volume

128

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

235-247

UT code for WoS article

000717744500007

EID of the result in the Scopus database

2-s2.0-85118341424