Towards a Low-Cost Remote Memory Attestation for the Smart Grid

Sensors

Xinyu Yang³  Xiaofei He³  Wei Yu²  Jie Lin³  Rui Li³  Qingyu Yang⁵  Houbing Song⁴  Yunchuan Sun¹  Antonio Jara¹ 

[1] Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China;;Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA; E-Mail:;Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China; E-Mails:;Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV 25136, USA; E-Mail:;SKLMSE Lab, School of Electronic Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; E-Mail:

关键词: smart measurement devices;    code injection attack;    software-based attestation;    smart grid;   

DOI  :  10.3390/s150820799

来源: mdpi

【 摘 要 】

In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.