GRU-based deep learning approach for network intrusion alert prediction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

GRU-based deep learning approach for network intrusion alert prediction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

GRU-based deep learning approach for network intrusion alert prediction

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Future Generation Computer Systems

ISSN

0167-739X

e-ISSN

—

Svazek periodika

Neuveden

Číslo periodika v rámci svazku

128

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

235-247

Kód UT WoS článku

000717744500007

EID výsledku v databázi Scopus

2-s2.0-85118341424