Consensus-Based Distributed Quickest Detection of Attacks with Unknown Parameters
Abstract
Sequential attack detection in a distributed sensor network is considered, where each sensor successively produces one-bit quantized samples of a desired deterministic scalar parameter corrupted by additive noise. The unknown parameters in the pre-attack and post-attack models, namely the desired parameter to be estimated and the injected malicious data at the attacked sensors pose a significant challenge for designing a computationally efficient scheme for each sensor to detect the occurrence of attacks by only using local communication with neighboring sensors. The generalized Cumulative Sum (GCUSUM) algorithm is considered, which replaces the unknown parameters with their maximum likelihood estimates in the CUSUM test statistic. For the problem under consideration, a sufficient condition is provided under which the expected false alarm period of the GCUSUM can be guaranteed to be larger than any given value. Next, we consider the distributed implementation of the GCUSUM. We first propose an alternative test statistic which is asymptotically equivalent to that of GCUSUM. Then based on the proposed alternative test statistic and running consensus algorithms, we propose a distributed approximate GCUSUM algorithm which significantly reduce the prohibitively high computational complexity of the centralized GCUSUM. Numerical results show that the proposed distributed approximate GCUSUM algorithm can provide a performance that is comparable to the centralized GCUSUM.
Recommended Citation
J. Zhang and X. Wang, "Consensus-Based Distributed Quickest Detection of Attacks with Unknown Parameters," IEEE Transactions on Information Theory, vol. 67, no. 3, pp. 1864 - 1885, Institute of Electrical and Electronics Engineers (IEEE), Dec 2020.
The definitive version is available at https://doi.org/10.1109/TIT.2020.3047353
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Approximation Algorithms; Consensus Algorithm; Cyberattack; Data Models; Distributed Databases; Distributed Quickest Detection; Generalized CUSUM; One-Bit Quantization; Power Demand; Quantization (Signal); Running Consensus Algorithms; Security
International Standard Serial Number (ISSN)
0018-9448; 1557-9654
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
24 Dec 2020
