Luke Dotson | Searcharxiv

arXiv2026

CTF for education

In this paper, we take a close look at how CTF can be used in cybersecurity education. We divide the CTF competitions into four different categories, which are attack-based CTFs, defense-based CTFs, jeopardy CTFs and gamified and wargames CTFs. We start our analysis by summarizing the main characteristics of different CTF types. We then compare them with each other in both learning objectives and other aspects like accessibility. We conclude that combining all four CTF formats can help participants build one's cybersecurity knowledge. By doing that, we hope that our findings will provide some useful insights for future CTF educators.

cs.CR↗

arXiv2024

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way

Previous studies on two-timescale stochastic approximation (SA) mainly focused on bounding mean-squared errors under diminishing stepsize schemes. In this work, we investigate {\it constant} stpesize schemes through the lens of Markov processes, proving that the iterates of both timescales converge to a unique joint stationary distribution in Wasserstein metric. We derive explicit geometric and non-asymptotic convergence rates, as well as the variance and bias introduced by constant stepsizes in the presence of Markovian noise. Specifically, with two constant stepsizes $\alpha < \beta$, we show that the biases scale linearly with both stepsizes as $\Theta(\alpha)+\Theta(\beta)$ up to higher-order terms, while the variance of the slower iterate (resp., faster iterate) scales only with its own stepsize as $O(\alpha)$ (resp., $O(\beta)$). Unlike previous work, our results require no additional assumptions such as $\beta^2 \ll \alpha$ nor extra dependence on dimensions. These fine-grained characterizations allow tail-averaging and extrapolation techniques to reduce variance and bias, improving mean-squared error bound to $O(\beta^4 + \frac{1}{t})$ for both iterates.

eess.SY↗