ترغب بنشر مسار تعليمي؟ اضغط هنا

Robust Detection of Biasing Attacks on Misappropriated Distributed Observers via Decentralized $H_infty$ synthesis

83   0   0.0 ( 0 )
 نشر من قبل Valery Ugrinovskii
 تاريخ النشر 2017
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English
 تأليف V. Ugrinovskii




اسأل ChatGPT حول البحث

We develop a decentralized $H_infty$ 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 $H_infty$ approach. Next, the attack detectors are embedded into the network, which allows them to detect misappropriated nodes from innovation in the network interconnections.



قيم البحث

اقرأ أيضاً

120 - V. Ugrinovskii 2018
We consider the distributed $H_infty$ 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 obs erver 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.
The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an $H_infty$ approach.
60 - Valery Ugrinovskii 2019
We consider the distributed $H_infty$ estimation problem with an 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. The paper proposes a procedure for the derivation of a distributed observer which endows each node with an attack detector which also functions as an attack compensating feedback controller for the main observer. Connecting these controlled observers into a network results in a distributed observer whose nodes produce unbiased robust estimates of the plant. We show that the gains for each controlled observer in the network can be computed in a decentralized fashion, thus reducing vulnerability of the network.
The paper considers a problem of detecting and mitigating biasing attacks on networks of state observers targeting cooperative state estimation algorithms. The problem is cast within the recently developed framework of distributed estimation utilizin g the vector dissipativity approach. The paper shows that a network of distributed observers can be endowed with an additional attack detection layer capable of detecting biasing attacks and correcting their effect on estimates produced by the network. An example is provided to illustrate the performance of the proposed distributed attack detector.
In this work, we present an analysis of the Burst failure effect in the $H_infty$ norm. We present a procedure to perform an analysis between different Markov Chain models and a numerical example. In the numerical example the results obtained pointed out that the burst failure effect in the performance does not exceed 6.3%. However, this work is an introduction for a wider and more extensive analysis in this subject.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا