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| Main Author: | |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.00481 |
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| _version_ | 1866918532730585088 |
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| author | Kim, Song-Kyoo |
| author_facet | Kim, Song-Kyoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario Kim, Song-Kyoo Cryptography and Security Systems and Control Probability Applications 60C55, 60K10, 90B15, 90B50, 91A35, 91A55, 93A30 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. |
| title | Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario |
| topic | Cryptography and Security Systems and Control Probability Applications 60C55, 60K10, 90B15, 90B50, 91A35, 91A55, 93A30 |
| url | https://arxiv.org/abs/2606.00481 |