Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center

Seema G. Aarella; Saraju P. Mohanty; Elias Kougianos; Deepak Puthal

doi:10.1109/ISVLSI59464.2023.10238517

Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center

Seema G. Aarella, Saraju P. Mohanty, Elias Kougianos, Deepak Puthal

University of North Texas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

A collaborative edge computing model involves multiple edge devices, such as Edge Data Centers (EDCs) and Edge Routers. Data can be stored and processed at the Edge in Collaborative Edge Computing (CEC). Edge security is very crucial as it is prone to external attacks at different layers of its architecture. In this work, a novel machine-learning-based approach is proposed to improve the security of EDCs using Security-by-Design (SbD) principle in CEC framework. This research aims to provide a security scheme for EDCs in a collaborative edge computing environment through machine-learning-based monitoring and authentication. Through this method, any drastic change in the device's behavior will be considered critical, and the device can be removed from the network, securing the network from further harm. This research proposes a support vector machine (SVM)-based algorithm to monitor the EDC authentication process and detect an intrusion or malicious authentication requests during load balancing.

Original language	British English
Title of host publication	2023 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Proceedings
Editors	Fernanda Kastensmidt, Ricardo Reis, Aida Todri-Sanial, Hai Li, Carolina Metzler
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350327694
DOIs	https://doi.org/10.1109/ISVLSI59464.2023.10238517
State	Published - 2023
Event	26th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Foz do Iguacu, Brazil Duration: 20 Jun 2023 → 23 Jun 2023

Publication series

Name	Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume	2023-June
ISSN (Print)	2159-3469
ISSN (Electronic)	2159-3477

Conference

Conference	26th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023
Country/Territory	Brazil
City	Foz do Iguacu
Period	20/06/23 → 23/06/23

Keywords

Collaborative Edge Computing
Device Monitoring
Edge Data Centers
Machine Learning
Secure Authentication
Security-by-Design (SbD)

Access to Document

10.1109/ISVLSI59464.2023.10238517

Cite this

Aarella, S. G., Mohanty, S. P., Kougianos, E., & Puthal, D. (2023). Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center. In F. Kastensmidt, R. Reis, A. Todri-Sanial, H. Li, & C. Metzler (Eds.), 2023 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Proceedings (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI; Vol. 2023-June). IEEE Computer Society. https://doi.org/10.1109/ISVLSI59464.2023.10238517

Aarella, Seema G. ; Mohanty, Saraju P. ; Kougianos, Elias et al. / Fortified-Edge 2.0 : Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center. 2023 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Proceedings. editor / Fernanda Kastensmidt ; Ricardo Reis ; Aida Todri-Sanial ; Hai Li ; Carolina Metzler. IEEE Computer Society, 2023. (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI).

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title = "Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center",

abstract = "A collaborative edge computing model involves multiple edge devices, such as Edge Data Centers (EDCs) and Edge Routers. Data can be stored and processed at the Edge in Collaborative Edge Computing (CEC). Edge security is very crucial as it is prone to external attacks at different layers of its architecture. In this work, a novel machine-learning-based approach is proposed to improve the security of EDCs using Security-by-Design (SbD) principle in CEC framework. This research aims to provide a security scheme for EDCs in a collaborative edge computing environment through machine-learning-based monitoring and authentication. Through this method, any drastic change in the device's behavior will be considered critical, and the device can be removed from the network, securing the network from further harm. This research proposes a support vector machine (SVM)-based algorithm to monitor the EDC authentication process and detect an intrusion or malicious authentication requests during load balancing.",

keywords = "Collaborative Edge Computing, Device Monitoring, Edge Data Centers, Machine Learning, Secure Authentication, Security-by-Design (SbD)",

author = "Aarella, {Seema G.} and Mohanty, {Saraju P.} and Elias Kougianos and Deepak Puthal",

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year = "2023",

doi = "10.1109/ISVLSI59464.2023.10238517",

language = "British English",

series = "Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI",

publisher = "IEEE Computer Society",

editor = "Fernanda Kastensmidt and Ricardo Reis and Aida Todri-Sanial and Hai Li and Carolina Metzler",

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Aarella, SG, Mohanty, SP, Kougianos, E & Puthal, D 2023, Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center. in F Kastensmidt, R Reis, A Todri-Sanial, H Li & C Metzler (eds), 2023 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Proceedings. Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI, vol. 2023-June, IEEE Computer Society, 26th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023, Foz do Iguacu, Brazil, 20/06/23. https://doi.org/10.1109/ISVLSI59464.2023.10238517

Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center. / Aarella, Seema G.; Mohanty, Saraju P.; Kougianos, Elias et al.
2023 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Proceedings. ed. / Fernanda Kastensmidt; Ricardo Reis; Aida Todri-Sanial; Hai Li; Carolina Metzler. IEEE Computer Society, 2023. (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Aarella, Seema G.

AU - Mohanty, Saraju P.

AU - Kougianos, Elias

AU - Puthal, Deepak

PY - 2023

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N2 - A collaborative edge computing model involves multiple edge devices, such as Edge Data Centers (EDCs) and Edge Routers. Data can be stored and processed at the Edge in Collaborative Edge Computing (CEC). Edge security is very crucial as it is prone to external attacks at different layers of its architecture. In this work, a novel machine-learning-based approach is proposed to improve the security of EDCs using Security-by-Design (SbD) principle in CEC framework. This research aims to provide a security scheme for EDCs in a collaborative edge computing environment through machine-learning-based monitoring and authentication. Through this method, any drastic change in the device's behavior will be considered critical, and the device can be removed from the network, securing the network from further harm. This research proposes a support vector machine (SVM)-based algorithm to monitor the EDC authentication process and detect an intrusion or malicious authentication requests during load balancing.

AB - A collaborative edge computing model involves multiple edge devices, such as Edge Data Centers (EDCs) and Edge Routers. Data can be stored and processed at the Edge in Collaborative Edge Computing (CEC). Edge security is very crucial as it is prone to external attacks at different layers of its architecture. In this work, a novel machine-learning-based approach is proposed to improve the security of EDCs using Security-by-Design (SbD) principle in CEC framework. This research aims to provide a security scheme for EDCs in a collaborative edge computing environment through machine-learning-based monitoring and authentication. Through this method, any drastic change in the device's behavior will be considered critical, and the device can be removed from the network, securing the network from further harm. This research proposes a support vector machine (SVM)-based algorithm to monitor the EDC authentication process and detect an intrusion or malicious authentication requests during load balancing.

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A2 - Todri-Sanial, Aida

A2 - Li, Hai

A2 - Metzler, Carolina

PB - IEEE Computer Society

Y2 - 20 June 2023 through 23 June 2023

ER -

Aarella SG, Mohanty SP, Kougianos E, Puthal D. Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center. In Kastensmidt F, Reis R, Todri-Sanial A, Li H, Metzler C, editors, 2023 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023 - Proceedings. IEEE Computer Society. 2023. (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI). doi: 10.1109/ISVLSI59464.2023.10238517

Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center

Abstract

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Keywords

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