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Within the range, for each pair of, we run simulations to identify the maximum attack probability that the attacker can use and avoid being detected.
For the case of randomly launched deception attacks, the desired attack probability is obtained by solving a Riccati-like equation.
The formation of TiO2 not only protected the Ti metal particles but also helped to improve the corrosion resistance of the composite coatings, because the TiO2 coated Ti microparticles decreased the attack probability of nickel deposits by corrosive medium.
Specifically, for a class of Kalman filters with χ2 detectors, an attacker with the given attack task needs to decide how many the maximum number is or how much the attack probability is for different kinds of attack scenarios (i.e. consecutive deception attacks or randomly launched deception attacks).
Malicious users' stealthiness deteriorates with the attack probability p a.
As or increases, the maximum allowed attack probability decreases.
Briefly, destructiveness in Figure2 increases with the attack probability while stealthiness in Figure5 decreases with the attack probability.
Obviously, for an honest SU, the attack probability equals to zero.
For a malicious SU, the attack probability p i ∈ 0,1).
The attacks are modeled by three parameters: the attack threshold, attack strength, and attack probability.
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In order to set up the relation between the intention of attack behaviors and the randomness of continuous-time Markov chain (CTMC), we construct an attack-prediction stochastic game that is able to attain the attack probabilities adopted by the attacker in different states.
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CEO of Professional Science Editing for Scientists @ prosciediting.com