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

Information Technology

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

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 Computer Society
Sivut	279-284
Sivumäärä	6
ISBN (elektroninen)	9798350339826
DOI - pysyväislinkit	https://doi.org/10.1109/CloudCom59040.2023.00052
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023 - Naples, Italy Kesto: 4 jouluk. 2023 → 6 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	14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
Maa/Alue	Italy
Kaupunki	Naples
Ajanjakso	04/12/23 → 06/12/23

Pääsy asiakirjaan

10.1109/CloudCom59040.2023.00052

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

VeriDevOps: Automated Protection and Prevention to Meet Security Requirements in DevOps Environments
Truscan, D., Porres Paltor, I., Ashraf, A., Ahmad, T., Chariyarupadannayil Sudheerbabu, G. & Chapagain, S.
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 Computer Society. 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 Computer Society, 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.; 14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023 ; Conference date: 04-12-2023 Through 06-12-2023",

year = "2023",

doi = "10.1109/CloudCom59040.2023.00052",

language = "English",

series = "Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom",

publisher = "IEEE Computer Society",

pages = "279--284",

booktitle = "Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023",

}

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 Computer Society, Sivut 279-284, 14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023, Naples, Italy, 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 Computer Society, 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 - Conference contribution

AN - SCOPUS:85189648043

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

SP - 279

EP - 284

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

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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 Computer Society. 2023. s. 279-284. (Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom). doi: 10.1109/CloudCom59040.2023.00052

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

Abstrakti

Julkaisusarja

Konferenssi

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

VeriDevOps: Automated Protection and Prevention to Meet Security Requirements in DevOps Environments

Viittausmuodot