Preliminary Results in Using Attention for Increasing Attack Identification Efficiency

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

1 Sitaatiot (Scopus)
123 Lataukset (Pure)

Abstrakti

In previous work, we proposed an end-to-end intrusion early detection system to identify network attacks in real-time before they are complete and could cause more damage to the system under attack. To implement the approach, we have used a deep neural network which was trained in a supervised manner to extract relevant features from raw network traffic in order to classify network flows into different types of attacks. In this work, we discuss the initial results of the benefits that an attention mechanism brings to the classification performance and the capacity of the network to detect attacks earlier. We empirically evaluate our approach on the CICIDS2017 dataset. Preliminary results show that the attention mechanism improves both the balanced accuracy of the classifier as well as the early detection of attacks.
AlkuperäiskieliEnglanti
Otsikko2023 IEEE International Conference on Software Testing, Verification and Validation
KustantajaIEEE
Sivut159-164
Sivumäärä6
ISBN (painettu)979-8-3503-3336-7
DOI - pysyväislinkit
TilaJulkaistu - toukok. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Software Testing, Verification and Validation - Dublin, Ireland
Kesto: 16 huhtik. 202320 huhtik. 2023
Konferenssinumero: 16
https://conf.researchr.org/home/icst-2023

Julkaisusarja

NimiIEEE International Conference on Software Testing, Verification and Validation Workshops
KustantajaIEEE
ISSN (painettu)2159-4848

Konferenssi

KonferenssiIEEE International Conference on Software Testing, Verification and Validation
LyhennettäICST
Maa/AlueIreland
KaupunkiDublin
Ajanjakso16/04/2320/04/23
www-osoite

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