Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario
For cybersecurity practitioners, this provides a mathematical tool to calibrate observation parameters for low-latency proactive defense, though the single-attack assumption limits real-world applicability.
This paper develops a stochastic framework for proactive cybersecurity defense timing under a single attack scenario, deriving closed-form expressions for defense moment density and conditional expectations. The results enable quantitative assessment of defense timing sensitivity to threat intensity.
This research presents a novel stochastic framework for proactive cybersecurity defense timing under a single attack scenario. The approach models the defense process as a continuous observation mechanism in which the defense instant and the subsequent observation slot follow independent exponential distributions. Laplace-Carson transforms combined with first-excess theory yield the joint detection function that brackets the attack moment. Marginalization under Markovian Poisson arrivals then produces the probability density of the defense moment and conditional expectations of pre-attack and post-attack observation times. These closed-form results enable quantitative assessment of defense timing sensitivity to threat intensity and support precise calibration of observation parameters for low-latency proactive measures. Major contributions include the explicit derivation of marginal distributions and expected values, visualization of defense moment density, and the bridging of stochastic duel methodology with practical cybersecurity applications.