An Evaluation of  Transformer Models for Early Intrusion Detection in Cloud Continuum

Md Mahbub Islam; Tanwir Ahmad; Dragos Truscan

doi:10.1109/CloudCom59040.2023.00052

An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum

Md Mahbub Islam^*
, Tanwir Ahmad
, Dragos Truscan

^*Tämän työn vastaava kirjoittaja

Informaatioteknologia

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

3 Sitaatiot (Scopus)

Abstrakti

With the increasing popularity of the cloud continuum, the security of different layers and nodes involved has become more relevant than ever. Intrusion detection systems, are one of the main tools to identify and intercept intrusion attacks. Furthermore, identifying the attacks in time, before they are completed, is necessary in order to deploy countermeasures in time and to limit the losses. In this work, we evaluate the use of transformer models for implementing early-detection signature-based detection systems targeted at Cloud Continuum. We implement the approach in the context of our tool for early detection of network intrusions and we evaluate it using the CICIDS2017 dataset and MQTT-IDS-2020. The results show that transformer models are a viable alternative for early-detection systems and this will pave the road for further research on the topic.

Alkuperäiskieli	Englanti
Otsikko	Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
Kustantaja	IEEE
Sivut	279-284
Sivumäärä	6
ISBN (elektroninen)	979-8-3503-3982-6
ISBN (painettu)	979-8-3503-3983-3
DOI - pysyväislinkit	https://doi.org/10.1109/CloudCom59040.2023.00052
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	IEEE International Conference on Cloud Computing Technology and Science - Kesto: 4 jouluk. 2023 → …

Julkaisusarja

Nimi	Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
ISSN (painettu)	2330-2194
ISSN (elektroninen)	2330-2186

Konferenssi

Konferenssi	IEEE International Conference on Cloud Computing Technology and Science
Lyhennettä	CloudCom
Ajanjakso	04/12/23 → …

Pääsy asiakirjaan

10.1109/CloudCom59040.2023.00052

Muut tiedostot ja linkit

Author version

Linkki julkaisuun Scopuksessa

1 Päättynyt

VeriDevOps: Automated Protection and Prevention to Meet Security Requirements in DevOps Environments
Truscan, D. (Vastuullinen tutkija), Porres Paltor, I. (CoPI), Ashraf, A. (Vastuullinen tutkija), Ahmad, T. (CoI), Chariyarupadannayil Sudheerbabu, G. (CoI) & Chapagain, S. (Vastuullinen tutkija)
EU Horizon 2020
01/10/20 → 31/01/24
Projekti: EU

Viittausmuodot

Islam, M. M., Ahmad, T., & Truscan, D. (2023). An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum. teoksessa Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023 (Sivut 279-284). (Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom). IEEE. https://doi.org/10.1109/CloudCom59040.2023.00052

Islam, Md Mahbub ; Ahmad, Tanwir ; Truscan, Dragos. / An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum. Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023. IEEE, 2023. Sivut 279-284 (Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom).

@inproceedings{4daa89c7025d4004b0f3444915e31a54,

title = "An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum",

abstract = "With the increasing popularity of the cloud continuum, the security of different layers and nodes involved has become more relevant than ever. Intrusion detection systems, are one of the main tools to identify and intercept intrusion attacks. Furthermore, identifying the attacks in time, before they are completed, is necessary in order to deploy countermeasures in time and to limit the losses. In this work, we evaluate the use of transformer models for implementing early-detection signature-based detection systems targeted at Cloud Continuum. We implement the approach in the context of our tool for early detection of network intrusions and we evaluate it using the CICIDS2017 dataset and MQTT-IDS-2020. The results show that transformer models are a viable alternative for early-detection systems and this will pave the road for further research on the topic.",

keywords = "deep learning, Intrusion detection systems, monitoring, transformer-architecture",

author = "Islam, \{Md Mahbub\} and Tanwir Ahmad and Dragos Truscan",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; IEEE International Conference on Cloud Computing Technology and Science, CloudCom ; Conference date: 04-12-2023",

year = "2023",

doi = "10.1109/CloudCom59040.2023.00052",

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Islam, MM, Ahmad, T & Truscan, D 2023, An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum. julkaisussa Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023. Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, IEEE, Sivut 279-284, IEEE International Conference on Cloud Computing Technology and Science, 04/12/23. https://doi.org/10.1109/CloudCom59040.2023.00052

An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum. / Islam, Md Mahbub; Ahmad, Tanwir ; Truscan, Dragos.
Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023. IEEE, 2023. s. 279-284 (Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom).

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum

AU - Islam, Md Mahbub

AU - Ahmad, Tanwir

AU - Truscan, Dragos

PY - 2023

Y1 - 2023

N2 - With the increasing popularity of the cloud continuum, the security of different layers and nodes involved has become more relevant than ever. Intrusion detection systems, are one of the main tools to identify and intercept intrusion attacks. Furthermore, identifying the attacks in time, before they are completed, is necessary in order to deploy countermeasures in time and to limit the losses. In this work, we evaluate the use of transformer models for implementing early-detection signature-based detection systems targeted at Cloud Continuum. We implement the approach in the context of our tool for early detection of network intrusions and we evaluate it using the CICIDS2017 dataset and MQTT-IDS-2020. The results show that transformer models are a viable alternative for early-detection systems and this will pave the road for further research on the topic.

AB - With the increasing popularity of the cloud continuum, the security of different layers and nodes involved has become more relevant than ever. Intrusion detection systems, are one of the main tools to identify and intercept intrusion attacks. Furthermore, identifying the attacks in time, before they are completed, is necessary in order to deploy countermeasures in time and to limit the losses. In this work, we evaluate the use of transformer models for implementing early-detection signature-based detection systems targeted at Cloud Continuum. We implement the approach in the context of our tool for early detection of network intrusions and we evaluate it using the CICIDS2017 dataset and MQTT-IDS-2020. The results show that transformer models are a viable alternative for early-detection systems and this will pave the road for further research on the topic.

KW - deep learning

KW - Intrusion detection systems

KW - monitoring

KW - transformer-architecture

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U2 - 10.1109/CloudCom59040.2023.00052

DO - 10.1109/CloudCom59040.2023.00052

M3 - Published conference proceeding

AN - SCOPUS:85189648043

SN - 979-8-3503-3983-3

T3 - Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom

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BT - Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023

PB - IEEE

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Islam MM, Ahmad T , Truscan D. An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum. julkaisussa Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023. IEEE. 2023. s. 279-284. (Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom). doi: 10.1109/CloudCom59040.2023.00052