Cyber security has emerged as one of the most important issues in unmanned aerial systems for which the functionality heavily relies on onboard automation and intervehicle communications. In this paper, potential cyber threats and vulnerabilities in the unmanned aerial system's state estimator to stealthy cyber attacks are identified, which can avoid being detected by the monitoring system. Specifically, this paper investigates the worst stealthy cyber attack that can maximize the state estimation error of the unmanned aerial system's state estimator while not being detected. First, the condition that the system is vulnerable to the stealthy cyber attacks is derived, and then an analytical method is provided to identify the worst stealthy cyber attack. The proposed cyber attack analysis methods are demonstrated with illustrative examples of an onboard unmanned aerial system navigation system and an unmanned aerial system tracking application. Nomenclature A, B = system matrices a c , a o = cyber attack vectors B c , B o = cyber attack matrices C = observation matrix E 1 , E 2 = noise input matrices e a = estimation error subject to cyber attacks F = equality constraint matrix h = threshold value J, Φ = objective functions k = time index L = steady-state Kalman gain L = Lagrange function N = discrete-time horizon Q = process noise covariance matrix Q oa , Q ca = controllability matrices R = measurement noise covariance matrix r = residual vector u = input vector v = measurement noise vector W c = controllability gramian w = process noise vector x a = state vector subject to cyber attackŝ x a = state estimate subject to cyber attacks y a = output vector subject to cyber attacks μ, ν = Lagrange multipliers Σ = estimation error covariance matrix Σ p = predicted error covariance matrix Σ r = residual covariance matrix X = optimization variable Ω = inequality constraint function

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Security of Cyber-Physical Systems (CPS) against cyber attacks is an important yet challenging problem. Since most cyber attacks happen in erratic ways, it is difficult to describe them systematically. In this paper, instead of identifying a specific cyber attack model, we are focused on analyzing the system's response during cyber attacks. Deception attacks (or false data injection attacks), which are performed by tampering with system components or data, are not of particular concern if they can be easily detected by the system's monitoring system. However, intelligent cyber attackers can avoid being detected by the monitoring system by carefully design cyber attacks. Our main objective is to investigate the performance of such stealthy deception attacks from the system's perspective. We investigate three kinds of stealthy deception attacks according to the attacker's ability to compromise the system. Based on the information about the dynamics of the system and existing hypothesis testing algorithms, we derive the necessary and sufficient conditions under which the attacker could perform each kind of attack without being detected. In the end, we illustrate the threat of these cyber attacks using an Unmanned Aerial Vehicle (UAV) navigation example.

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