Abstract
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.
Highlights
With the advance of information and network technologies, the Internet of Things (IoT) has revolutionized many conventional areas, including smart home, intelligent transportation and smart grid [1,2,3]
We propose a low-cost remote memory attestation scheme (LRMA) to verify remote nodes in the advanced metering infrastructure (AMI) network in the smart grid
In order to eliminate the impact of network delay, we develop a delay-resilient remote memory attestation scheme, which can evaluate the real-time network delay by using the time differences reported by relay nodes in the process of the “challenge-response” protocol
Summary
With the advance of information and network technologies, the Internet of Things (IoT) has revolutionized many conventional areas, including smart home, intelligent transportation and smart grid [1,2,3]. Smart measurement devices (e.g., smart meters), deployed on the user side of the smart grid and applied to measure the power usage information, can enhance the interactivity between utilities and customers, and can increase the efficiency of energy consumption. Measurement devices (e.g., meters or sensors) can be compromised by cyber attacks (e.g., malicious code injection, etc.) launched by adversaries, because they may be connected through computer networks [6]. The adversary can launch additional attacks (e.g., injecting false energy demand information) to disrupt the operations of the smart grid (e.g., the dispatch of energy [7], the electricity market [8], etc.). As the smart grid consists of a large number of measurement devices, how to detect compromised remote devices efficiently and accurately is a critical issue
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