Secure distributed observers for a class of linear time invariant systems in the presence of Byzantine adversaries

We study the problem of distributed state estimation of a linear time-invariant system by a network of nodes, some of which are subject to adversarial attacks. We develop a secure distributed estimation strategy subject to an f-locally bounded Byzantine adversary model, where a compromised node can arbitrarily deviate from the rules of any prescribed algorithm. Under such a threat model, we provide sufficient conditions guaranteeing the success of our estimation strategy. Our method relies on the construction of a subgraph, which we call a Mode Estimation Directed Acyclic Graph (MEDAG), for each unstable and marginally stable eigenvalue of the plant. We provide a distributed algorithm for constructing a MEDAG and characterize graph topologies for which the construction algorithm is guaranteed to succeed. Our approach provides fundamental insights into the relationship between the dynamics of the system, the measurement structure of the nodes, and the underlying graph topology.

[1]  Shreyas Sundaram,et al.  Resilient Asymptotic Consensus in Robust Networks , 2013, IEEE Journal on Selected Areas in Communications.

[2]  Andrzej Pelc,et al.  Broadcasting with locally bounded Byzantine faults , 2005, Inf. Process. Lett..

[3]  Lewis Tseng,et al.  Broadcast using certified propagation algorithm in presence of Byzantine faults , 2012, Information Processing Letters.

[4]  Nitin H. Vaidya,et al.  Byzantine Multi-Agent Optimization: Part I , 2015, ArXiv.

[5]  Shinkyu Park,et al.  Design of Distributed LTI Observers for State Omniscience , 2017, IEEE Transactions on Automatic Control.

[6]  Brian D. O. Anderson,et al.  Algebraic characterization of fixed modes in decentralized control , 1981, Autom..

[7]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[8]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[9]  Shinkyu Park,et al.  A Class of LTI Distributed Observers for LTI Plants: Necessary and Sufficient Conditions for Stabilizability , 2014, ArXiv.

[10]  Usman A. Khan,et al.  Secure distributed estimation in cyber-physical systems , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Lewis Tseng,et al.  Iterative approximate byzantine consensus in arbitrary directed graphs , 2012, PODC '12.

[12]  Shreyas Sundaram,et al.  Robustness of information diffusion algorithms to locally bounded adversaries , 2011, 2012 American Control Conference (ACC).

[13]  Shreyas Sundaram,et al.  Consensus-based distributed optimization with malicious nodes , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[14]  Nitin H. Vaidya,et al.  Byzantine Multi-Agent Optimization: Part II , 2015, ArXiv.

[15]  Usman A. Khan,et al.  Collaborative scalar-gain estimators for potentially unstable social dynamics with limited communication , 2014, Autom..

[16]  Karl Henrik Johansson,et al.  Distributed fault detection for interconnected second-order systems , 2011, Autom..

[17]  John S. Baras,et al.  Trust-based multi-agent filtering for increased Smart Grid security , 2011, 2012 20th Mediterranean Conference on Control & Automation (MED).

[18]  John S. Baras,et al.  A Trust Based Distributed Kalman Filtering Approach for Mode Estimation in Power Systems , 2010 .

[19]  Reza Olfati-Saber,et al.  Distributed Kalman filtering for sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[20]  Soummya Kar,et al.  On connectivity, observability, and stability in distributed estimation , 2010, 49th IEEE Conference on Decision and Control (CDC).

[21]  Chiu-Yuen Koo,et al.  Broadcast in radio networks tolerating byzantine adversarial behavior , 2004, PODC '04.

[22]  Shreyas Sundaram,et al.  Distributed Observers for LTI Systems , 2016, IEEE Transactions on Automatic Control.

[23]  Shinkyu Park,et al.  Necessary and sufficient conditions for the stabilizability of a class of LTI distributed observers , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[24]  Nancy A. Lynch,et al.  Reaching approximate agreement in the presence of faults , 1986, JACM.

[25]  Usman A. Khan,et al.  On the stability and optimality of distributed Kalman filters with finite-time data fusion , 2011, Proceedings of the 2011 American Control Conference.