Visible to the public A Direct Anonymous Attestation Scheme Based on Mimic Defense Mechanism

TitleA Direct Anonymous Attestation Scheme Based on Mimic Defense Mechanism
Publication TypeConference Paper
Year of Publication2020
AuthorsYu, Chen, Chen, Liquan, Lu, Tianyu
Conference Name2020 International Conference on Internet of Things and Intelligent Applications (ITIA)
Date PublishedNov. 2020
PublisherIEEE
ISBN Number978-1-7281-9301-4
Keywordsactive defense, anonymous attestation, attestation, composability, Hash functions, Human Behavior, Internet of Things, M2M, machine-to-machine communications, mimic defense, privacy, Protocols, pubcrawl, Public key, resilience, Resiliency, Servers
Abstract

Machine-to-Machine (M2M) communication is a essential subset of the Internet of Things (IoT). Secure access to communication network systems by M2M devices requires the support of a secure and efficient anonymous authentication protocol. The Direct Anonymous Attestation (DAA) scheme in Trustworthy Computing is a verified security protocol. However, the existing defense system uses a static architecture. The "mimic defense" strategy is characterized by active defense, which is not effective against continuous detection and attack by the attacker. Therefore, in this paper, we propose a Mimic-DAA scheme that incorporates mimic defense to establish an active defense scheme. Multiple heterogeneous and redundant actuators are used to form a DAA verifier and optimization is scheduled so that the behavior of the DAA verifier unpredictable by analysis. The Mimic-DAA proposed in this paper is capable of forming a security mechanism for active defense. The Mimic-DAA scheme effectively safeguard the unpredictability, anonymity, security and system-wide security of M2M communication networks. In comparison with existing DAA schemes, the scheme proposed in this paper improves the safety while maintaining the computational complexity.

URLhttps://ieeexplore.ieee.org/document/9312307
DOI10.1109/ITIA50152.2020.9312307
Citation Keyyu_direct_2020