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Herein, design of false data injection attack on a distributed cyber-physicalsystem is considered. A stochastic process with linear dynamics and Gaussiannoise is measured by multiple agent nodes, each equipped with multiple sensors.The agent nodes form a multi-hop network among themselves. Each agent nodecomputes an estimate of the process by using its sensor observation andmessages obtained from neighboring nodes,via Kalman-consensus filtering. Anexternal attacker, capable of arbitrarily manipulating the sensor observationsof some or all agent nodes, injects errors into those sensor observations. Thegoal of the attacker is to steer the estimates at the agent nodes as close aspossible to a pre-specified value, while respecting a constraint on the attackdetection probability. To this end,a constrained optimization problem isformulated to find the optimal parameter values of a certain class of linearattacks. The parameters of linear attack are learnt on-line via a combinationof stochastic approximation and online stochastic gradient descent.Numericalresults demonstrate the efficacy of the attack.

Optimal deception attack on networked vehicular cyber physical systems