NSGA-II-Based Granularity-Adaptive Control-Flow Attestation

Since the widespread adoption of edge computing and IoT technology, Control-Flow Hijacking (CFH) attacks targeting programs in resource-constrained embedded devices have become prevalent. While the Coarse-Grained Control-Flow integrity Attestation (CGCFA) lacks accuracy for the CFH attacks detection...

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Autores principales:	Jing Zhan, Yongzhen Li, Yifan Liu, Hongchao Li, Shuai Zhang, Li Lin
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/f0174519b3df48fca02b08e55ff6d51d
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spelling	oai:doaj.org-article:f0174519b3df48fca02b08e55ff6d51d2021-11-29T00:56:37ZNSGA-II-Based Granularity-Adaptive Control-Flow Attestation1939-012210.1155/2021/2914192https://doaj.org/article/f0174519b3df48fca02b08e55ff6d51d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2914192https://doaj.org/toc/1939-0122Since the widespread adoption of edge computing and IoT technology, Control-Flow Hijacking (CFH) attacks targeting programs in resource-constrained embedded devices have become prevalent. While the Coarse-Grained Control-Flow integrity Attestation (CGCFA) lacks accuracy for the CFH attacks detection, the Fine-Grained Control-Flow integrity Attestation (FGCFA) detect the attacks more accurately but with high overheads, which can be a big burden (e.g., to industrial control system with strict performance requirements). In this paper, we propose a NSGA-II (Nondominated Sorting Genetic Algorithm-II) based Granularity-Adaptive Control-Flow Attestation (GACFA) for the programs in embedded devices. Specifically, we propose a Granularity-Adaptive Control-Flow representation model to reduce the complexity of programs’ control-flow graph and propose NSGA-II-based granularity-adaptive strategy generation algorithm to balance the security and performance requirements. Besides, runtime protection for the GACFA at the program end with SGX is proposed to protect the integrity and confidentiality of control-flow measurement data. The experiments show that our work can find out the best-so-far control-flow granularity with stability and provide secure program attestation for the verifier. In addition, the security/performance benefit of adopting our proposal over CGCFA is 13.7, 25.1, and 43.0 times that of adopting FGCFA over ours in different threat scenarios.Jing ZhanYongzhen LiYifan LiuHongchao LiShuai ZhangLi LinHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Jing Zhan Yongzhen Li Yifan Liu Hongchao Li Shuai Zhang Li Lin NSGA-II-Based Granularity-Adaptive Control-Flow Attestation
description	Since the widespread adoption of edge computing and IoT technology, Control-Flow Hijacking (CFH) attacks targeting programs in resource-constrained embedded devices have become prevalent. While the Coarse-Grained Control-Flow integrity Attestation (CGCFA) lacks accuracy for the CFH attacks detection, the Fine-Grained Control-Flow integrity Attestation (FGCFA) detect the attacks more accurately but with high overheads, which can be a big burden (e.g., to industrial control system with strict performance requirements). In this paper, we propose a NSGA-II (Nondominated Sorting Genetic Algorithm-II) based Granularity-Adaptive Control-Flow Attestation (GACFA) for the programs in embedded devices. Specifically, we propose a Granularity-Adaptive Control-Flow representation model to reduce the complexity of programs’ control-flow graph and propose NSGA-II-based granularity-adaptive strategy generation algorithm to balance the security and performance requirements. Besides, runtime protection for the GACFA at the program end with SGX is proposed to protect the integrity and confidentiality of control-flow measurement data. The experiments show that our work can find out the best-so-far control-flow granularity with stability and provide secure program attestation for the verifier. In addition, the security/performance benefit of adopting our proposal over CGCFA is 13.7, 25.1, and 43.0 times that of adopting FGCFA over ours in different threat scenarios.
format	article
author	Jing Zhan Yongzhen Li Yifan Liu Hongchao Li Shuai Zhang Li Lin
author_facet	Jing Zhan Yongzhen Li Yifan Liu Hongchao Li Shuai Zhang Li Lin
author_sort	Jing Zhan
title	NSGA-II-Based Granularity-Adaptive Control-Flow Attestation
title_short	NSGA-II-Based Granularity-Adaptive Control-Flow Attestation
title_full	NSGA-II-Based Granularity-Adaptive Control-Flow Attestation
title_fullStr	NSGA-II-Based Granularity-Adaptive Control-Flow Attestation
title_full_unstemmed	NSGA-II-Based Granularity-Adaptive Control-Flow Attestation
title_sort	nsga-ii-based granularity-adaptive control-flow attestation
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/f0174519b3df48fca02b08e55ff6d51d
work_keys_str_mv	AT jingzhan nsgaiibasedgranularityadaptivecontrolflowattestation AT yongzhenli nsgaiibasedgranularityadaptivecontrolflowattestation AT yifanliu nsgaiibasedgranularityadaptivecontrolflowattestation AT hongchaoli nsgaiibasedgranularityadaptivecontrolflowattestation AT shuaizhang nsgaiibasedgranularityadaptivecontrolflowattestation AT lilin nsgaiibasedgranularityadaptivecontrolflowattestation
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NSGA-II-Based Granularity-Adaptive Control-Flow Attestation

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