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 › Konferensbidrag › Vetenskaplig › Peer review

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 Computer Society
Sidor	279-284
Antal sidor	6
ISBN (elektroniskt)	9798350339826
DOI	https://doi.org/10.1109/CloudCom59040.2023.00052
Status	Publicerad - 2023
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023 - Naples, Italien Varaktighet: 4 dec. 2023 → 6 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	14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
Land/Territorium	Italien
Ort	Naples
Period	04/12/23 → 06/12/23

Åtkomst av dokument

10.1109/CloudCom59040.2023.00052

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Länk till publikationen i Scopus

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
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 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. s. 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",

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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 Computer Society, s. 279-284, 14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023, Naples, Italien, 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).

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › 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.

KW - deep learning

KW - Intrusion detection systems

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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 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

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Projekt

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

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