2 research outputs found
Robust Detection of Biasing Attacks on Misappropriated Distributed Observers via Decentralized synthesis
We develop a decentralized synthesis approach to detection of
biasing misappropriation attacks on distributed observers. Its starting point
is to equip the observer with an attack model which is then used in the design
of attack detectors. A two-step design procedure is proposed. First, an initial
centralized setup is carried out which enables each node to compute the
parameters of its attack detector online in a decentralized manner, without
interacting with other nodes. Each such detector is designed using the
approach. Next, the attack detectors are embedded into the network,
which allows them to detect misappropriated nodes from innovation in the
network interconnections.Comment: To appear in the Proceedings of the 2017 Asian Control Conference
ASCC2017, Gold Coast, Australia, Dec 201
Resilient Distributed Estimation via Dynamic Rejection of Biasing Attacks
We consider the distributed estimation problem with additional
requirement of resilience to biasing attacks. An attack scenario is considered
where an adversary misappropriates some of the observer nodes and injects
biasing signals into observer dynamics. Using a dynamic modelling of biasing
attack inputs, a novel distributed state estimation algorithm is proposed which
involves feedback from a network of attack detection filters. We show that each
observer in the network can be computed in real time and in a decentralized
fashion. When these controlled observers are interconnected to form a network,
they are shown to cooperatively produce an unbiased estimate the plant, despite
some of the nodes are compromised.Comment: Accepted for presentation at the 2018 American Control Conference,
June 27-29, 2018, Milwaukee, US