Leveraging siamese networks for one-shot intrusion detection model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00361070" target="_blank" >RIV/68407700:21230/23:00361070 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s10844-022-00747-z" target="_blank" >https://doi.org/10.1007/s10844-022-00747-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10844-022-00747-z" target="_blank" >10.1007/s10844-022-00747-z</a>

Result language

angličtina

Original language name

Leveraging siamese networks for one-shot intrusion detection model

Original language description

The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems (IDS) has been the subject of significant research. Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to retrain the model for every unseen cyber-attack class. However, retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data. Although anomaly detection systems provide a coarse-grained defence against unseen attacks, these approaches are significantly less accurate and suffer from high false-positive rates. Here, a complementary approach referred to as "One-Shot Learning", whereby a limited number of examples of a new attack-class is used to identify a new attack-class (out of many) is detailed. The model grants a new cyber-attack classification opportunity for classes that were not seen during training without retraining. A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks. The performance of a pre-trained model to classify new attack-classes based only on one example is evaluated using three mainstream IDS datasets; CICIDS2017, NSL-KDD, and KDD Cup'99. The results confirm the adaptability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representations.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Journal of Intelligent Information Systems

ISSN

0925-9902

e-ISSN

1573-7675

Volume of the periodical

60

Issue of the periodical within the volume

2

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

30

Pages from-to

407-436

UT code for WoS article

000879127600001

EID of the result in the Scopus database

2-s2.0-85141377999