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Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario

arXiv Security Archived Jun 02, 2026 ✓ Full text saved

arXiv:2606.00481v1 Announce Type: new Abstract: 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. Marginal

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    Computer Science > Cryptography and Security [Submitted on 30 May 2026] Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario Song-Kyoo Kim 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. Comments: Target to submit an international journal Subjects: Cryptography and Security (cs.CR); Systems and Control (eess.SY); Probability (math.PR); Applications (stat.AP) MSC classes: 60C55, 60K10, 90B15, 90B50, 91A35, 91A55, 93A30 Cite as: arXiv:2606.00481 [cs.CR]   (or arXiv:2606.00481v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.00481 Focus to learn more Submission history From: Song-Kyoo Amang Kim Ph.D. [view email] [v1] Sat, 30 May 2026 02:22:22 UTC (758 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.SY eess eess.SY math math.PR stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 02, 2026
    Archived
    Jun 02, 2026
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