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Towards autonomous physical security defenses using machine learning

Towards autonomous physical security defenses using machine learning
Towards autonomous physical security defenses using machine learning
The sheer increase in interconnected devices, reaching 50 B in 2025, makes it easier for adversaries to have direct access to the target system and perform physical attacks. This risk is exacerbated by the proliferation of Internet-of-Battlefield Things (IoBT) and increased reliance on the use of embedded devices in critical infrastructure and industrial control systems. Existing anti-tamper designs protect against limited forms of attacks and have deterministic tamper responses, which can undermine the availability of systems. Advancements in physical inspection techniques have enabled stealthier attacks. Therefore, there is a pressing need for more intelligent defenses that ensure a longer operational time while keeping up with the expected increase in the capabilities of adversaries. This study proposes to enhance existing physical protection methods by developing an intelligent anti-tamper using machine learning algorithms. It uses an analytic system capable of detecting and classifying multiple types of behaviors (e.g., normal operation conditions, known attack vectors, and anomalous behavior). The system also has a layered response mechanism and recovery scheme, which reduces false alarms and prolongs the operational time. An experimental platform was constructed and used for data collection and machine learning model training. This study also explored the impact of adversarial learning attacks on the proposed system and subsequently developed a countermeasure. The final prototype was capable of recognizing two types of normal operating conditions (sheltered and exposed environments) and four types of physical attacks. It also has adaptive response and recovery mechanisms.
2169-3536
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Hall, Christian
f8cefd2b-2add-45da-be05-748bc9d0e140
Fathir, Syed
6664deac-f0f1-4c76-b810-c0b85f153e2e
Kit, Nelson
e07bfa8b-2204-4184-bc49-69521522eafe
Raymonde, Ruwaydah
d745e695-6e88-4fa4-9b0f-2dd499a373e7
Gimson, Michael
b2ee1c41-d50e-4f32-bdda-d536f6a12339
Kida, Ahmad
1cdbd60a-4cee-41d9-a8ec-980c6c36c749
Vincent, Hugo
1860345c-b91b-4500-9b3b-8b5e7ab01051
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Hall, Christian
f8cefd2b-2add-45da-be05-748bc9d0e140
Fathir, Syed
6664deac-f0f1-4c76-b810-c0b85f153e2e
Kit, Nelson
e07bfa8b-2204-4184-bc49-69521522eafe
Raymonde, Ruwaydah
d745e695-6e88-4fa4-9b0f-2dd499a373e7
Gimson, Michael
b2ee1c41-d50e-4f32-bdda-d536f6a12339
Kida, Ahmad
1cdbd60a-4cee-41d9-a8ec-980c6c36c749
Vincent, Hugo
1860345c-b91b-4500-9b3b-8b5e7ab01051

Halak, Basel, Hall, Christian, Fathir, Syed, Kit, Nelson, Raymonde, Ruwaydah, Gimson, Michael, Kida, Ahmad and Vincent, Hugo (2022) Towards autonomous physical security defenses using machine learning. IEEE Access, 10. (doi:10.1109/ACCESS.2022.3175615).

Record type: Article

Abstract

The sheer increase in interconnected devices, reaching 50 B in 2025, makes it easier for adversaries to have direct access to the target system and perform physical attacks. This risk is exacerbated by the proliferation of Internet-of-Battlefield Things (IoBT) and increased reliance on the use of embedded devices in critical infrastructure and industrial control systems. Existing anti-tamper designs protect against limited forms of attacks and have deterministic tamper responses, which can undermine the availability of systems. Advancements in physical inspection techniques have enabled stealthier attacks. Therefore, there is a pressing need for more intelligent defenses that ensure a longer operational time while keeping up with the expected increase in the capabilities of adversaries. This study proposes to enhance existing physical protection methods by developing an intelligent anti-tamper using machine learning algorithms. It uses an analytic system capable of detecting and classifying multiple types of behaviors (e.g., normal operation conditions, known attack vectors, and anomalous behavior). The system also has a layered response mechanism and recovery scheme, which reduces false alarms and prolongs the operational time. An experimental platform was constructed and used for data collection and machine learning model training. This study also explored the impact of adversarial learning attacks on the proposed system and subsequently developed a countermeasure. The final prototype was capable of recognizing two types of normal operating conditions (sheltered and exposed environments) and four types of physical attacks. It also has adaptive response and recovery mechanisms.

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Accepted/In Press date: 13 May 2022
e-pub ahead of print date: 16 May 2022

Identifiers

Local EPrints ID: 457594
URI: http://eprints.soton.ac.uk/id/eprint/457594
ISSN: 2169-3536
PURE UUID: fbd3c4aa-37fd-4bef-8eed-da3f59904ea1
ORCID for Basel Halak: ORCID iD orcid.org/0000-0003-3470-7226

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Date deposited: 13 Jun 2022 16:50
Last modified: 14 Jun 2022 01:41

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Contributors

Author: Basel Halak ORCID iD
Author: Christian Hall
Author: Syed Fathir
Author: Nelson Kit
Author: Ruwaydah Raymonde
Author: Michael Gimson
Author: Ahmad Kida
Author: Hugo Vincent

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