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

^*Korresponderande författare för detta arbete

Informationsteknologi

Forskningsoutput: Kapitel i bok/konferenshandling › Publicerad konferensartikel › Vetenskaplig › Peer review

1 Citeringar (Scopus)

Sammanfattning

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.

Originalspråk	Engelska
Titel på värdpublikation	Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
Förlag	IEEE
Sidor	279-284
Antal sidor	6
ISBN (elektroniskt)	979-8-3503-3982-6
ISBN (tryckt)	979-8-3503-3983-3
DOI	https://doi.org/10.1109/CloudCom59040.2023.00052
Status	Publicerad - 2023
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	IEEE International Conference on Cloud Computing Technology and Science - Varaktighet: 4 dec. 2023 → …

Publikationsserier

Namn	Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
ISSN (tryckt)	2330-2194
ISSN (elektroniskt)	2330-2186

Konferens

Konferens	IEEE International Conference on Cloud Computing Technology and Science
Förkortad titel	CloudCom
Period	04/12/23 → …

Åtkomst av dokument

10.1109/CloudCom59040.2023.00052

Andra filer och länkar

Länk till publikationen i Scopus

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

Citera det här

Islam, M. M., Ahmad, T., & Truscan, D. (2023). An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum. I Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023 (s. 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. s. 279-284 (Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom).

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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",

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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. i 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, s. 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).

Forskningsoutput: Kapitel i bok/konferenshandling › Publicerad konferensartikel › Vetenskaplig › Peer review

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AU - Islam, Md Mahbub

AU - Ahmad, Tanwir

AU - Truscan, Dragos

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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.

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Islam MM, Ahmad T , Truscan D. An Evaluation of Transformer Models for Early Intrusion Detection in Cloud Continuum. I 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