The phrase "attacking probabilities" is correct and usable in written English.
It can be used in contexts related to statistics, risk assessment, or decision-making where one is analyzing or challenging the likelihood of certain outcomes.
Example: "In our analysis, we will be attacking probabilities to better understand the risks involved in the project."
Alternatives: "challenging probabilities" or "questioning probabilities".
The attacker selects the attacking probability distribution P = { p1,p2,…,p K } over all relays in R, where p i is the probability of selecting R i as attacking target and K is the number of candidate relays in the radio coverage of the source.
We assume that the attacker prefers selecting relay R i with the attacking probability p i ∗ that maximizes u A (p i,q i ) as its attacking target; the attacker's attacking target selection may coincide with the source's relay selection.
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.
It is found that attack probabilities had to be much higher than currently observed to justify additional protective measures.
In the second case, attack probabilities are passed between system components as appropriate to model attacks that exploit vulnerabilities in multiple system components.
Based on these attack probabilities and the structure of the modular attack trees, risk measures can be estimated for the complete system and compared with the tolerable risk demanded by stakeholders.
Figure5 shows the evolution of the stealthiness metric, defined in (30), under different attack probabilities.
The first time you create a plan, security professionals estimate attack probabilities and how much might be lost if the attacks succeed.
Two main observations are as follows: After a few sensing slots, the stealthiness metric restrains itself to a constant value ψ for each given attack probability p a. Figure 5 Stealthiness under different attack probabilities.
After a few sensing slots, the stealthiness metric restrains itself to a constant value ψ for each given attack probability p a. Figure 5 Stealthiness under different attack probabilities.
