Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario
- URL: http://arxiv.org/abs/2606.00481v1
- Date: Sat, 30 May 2026 02:22:22 GMT
- Title: Stochastic Analysis of Cybersecurity Defense Strategies Under Single Attack Scenario
- Abstract summary: This research presents a novel framework for proactive cybersecurity defense timing under a single attack scenario.<n>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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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. 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.
Related papers
- Analytic Planning under Uncertainty with Moment Closure [32.403961716203455]
Propagating full state analytically offers a principled way to do this, but has traditionally required restrictive policy or reward structures to remain tractable.<n>We investigate whether distribution-aware planning is possible without these constraints.<n>Our approach reduces target variance yields well-calibrated predictive uncertainty under observations in continuous control.
arXiv Detail & Related papers (2026-08-03T17:17:00Z) - Secure-CHG: A Comprehensive Framework for Robust and Fair Federated Learning via Hybrid Defense and Contribution-Aware Trust [4.5294318938213465]
Federated Learning (FL) is highly susceptible to stealthy backdoor attacks.<n>We identify a fundamental vulnerability termed Late-stage Failure''<n>We propose Secure-CHG, a hybrid framework that pivots the defense paradigm from morphological detection toward intrinsic semantic contribution verification.
arXiv Detail & Related papers (2026-06-30T02:53:16Z) - Density-aware Sample-specific Attack [9.323723123910618]
We introduce principled criteria characterizing optimal sample-specific trigger construction under a Bayes-optimal model of the victim's training.<n>Our analysis reveals that both attack success and clean-accuracy preservation are simultaneously optimized when triggered samples are steered into low-density regions of the clean data distribution.
arXiv Detail & Related papers (2026-05-27T01:02:49Z) - Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy [51.758416625168]
Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations.<n>We propose a framework for certification of complex, composed mechanisms.<n>We provide a principled and general framework for using composite mechanisms to certify robustness under complex threat models.
arXiv Detail & Related papers (2026-05-20T22:17:29Z) - Defenses at Odds: Measuring and Explaining Defense Conflicts in Large Language Models [21.944459747448974]
We present the first systematic study of cross-defense interactions under sequential deployment.<n>We find that 38.9% exhibit measurable risk exacerbation on the originally defended dimension.<n>We propose a lightweight mitigation that selectively freezes high-conflict layers during sequential deployment.
arXiv Detail & Related papers (2026-05-14T07:58:47Z) - The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks [51.468144272905135]
Deep neural networks (DNNs) underpin critical applications yet remain vulnerable to backdoor attacks.<n>We provide a theoretical analysis targeting backdoor attacks, focusing on how sparse decision boundaries enable disproportionate model manipulation.<n>We propose Eminence, an explainable and robust black-box backdoor framework with provable theoretical guarantees and inherent stealth properties.
arXiv Detail & Related papers (2025-12-11T08:09:07Z) - Preliminary Investigation into Uncertainty-Aware Attack Stage Classification [81.28215542218724]
This work addresses the problem of attack stage inference under uncertainty.<n>We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages.<n>Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with confidence.
arXiv Detail & Related papers (2025-08-01T06:58:00Z) - Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models [12.524811181751577]
We propose a novel optimization for adversarial attacks against BCSD models.<n>In particular, we aim to improve the attacks in a challenging scenario, where the attack goal is to limit the model predictions to a specific range.<n>Our attack leverages the superior capability of black-box, model-agnostic explainers in interpreting the model decision boundaries.
arXiv Detail & Related papers (2025-06-05T08:29:19Z) - MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models.
Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs.
Empirical evaluations conducted on different datasets validate the efficacy of our approach.
arXiv Detail & Related papers (2024-06-13T15:55:04Z) - Learn from the Past: A Proxy Guided Adversarial Defense Framework with
Self Distillation Regularization [53.04697800214848]
Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models.
AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting.
We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
arXiv Detail & Related papers (2023-10-19T13:13:41Z) - Improving Adversarial Robustness to Sensitivity and Invariance Attacks
with Deep Metric Learning [80.21709045433096]
A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample.
We use metric learning to frame adversarial regularization as an optimal transport problem.
Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.
arXiv Detail & Related papers (2022-11-04T13:54:02Z) - Trust but Verify: Assigning Prediction Credibility by Counterfactual
Constrained Learning [123.3472310767721]
Prediction credibility measures are fundamental in statistics and machine learning.
These measures should account for the wide variety of models used in practice.
The framework developed in this work expresses the credibility as a risk-fit trade-off.
arXiv Detail & Related papers (2020-11-24T19:52:38Z) - Defending Regression Learners Against Poisoning Attacks [25.06658793731661]
We introduce a novel Local Intrinsic Dimensionality (LID) based measure called N-LID that measures the local deviation of a given data point's LID with respect to its neighbors.
N-LID can distinguish poisoned samples from normal samples and propose an N-LID based defense approach that makes no assumptions of the attacker.
We show that the proposed defense mechanism outperforms the state of the art defenses in terms of prediction accuracy (up to 76% lower MSE compared to an undefended ridge model) and running time.
arXiv Detail & Related papers (2020-08-21T03:02:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.