arXiv subjects

Nicolai Bissantz

Publications and source records attributed to Nicolai Bissantz.

At least 19 recordsLinked to original sources

arXiv2026

An Empirical Study of Fuzz Harness Degradation

The purpose of continuous fuzzing platforms is to enable fuzzing for software projects via fuzz harnesses -- but as the projects continue to evolve, are these harnesses updated in lockstep, or do they run out of date? If these harnesses remain unmaintained, will they degrade over time in terms of coverage achieved or number of bugs found? This is the subject of our study. We study Google's OSS-Fuzz continuous fuzzing platform containing harnesses for 510 open-source C/C++ projects, many of which are security-critical. A harness is the glue code between the fuzzer and the project, so it needs to adapt to changes in the project. It is often added by a project maintainer or as part of a, sometimes short-lived, testing effort. Our analysis shows a consistent overall fuzzer coverage percentage for projects in OSS-Fuzz and a surprising longevity of the bug-finding capability of harnesses even without explicit updates, as long as they still build. However, we also identify and manually examine individual cases of harness coverage degradation and categorize their root causes. Furthermore, we contribute to OSS-Fuzz and Fuzz Introspector to support metrics to detect harness degradation in OSS-Fuzz projects guided by this research.

cs.SE↗

arXiv2025

SILENT: A New Lens on Statistics in Software Timing Side Channels

Cryptographic research takes software timing side channels seriously. Approaches to mitigate them include constant-time coding and techniques to enforce such practices. However, recent attacks like Meltdown [42], Spectre [37], and Hertzbleed [70] have challenged our understanding of what it means for code to execute in constant time on modern CPUs. To ensure that assumptions on the underlying hardware are correct and to create a complete feedback loop, developers should also perform \emph{timing measurements} as a final validation step to ensure the absence of exploitable side channels. Unfortunately, as highlighted by a recent study by Jancar et al. [30], developers often avoid measurements due to the perceived unreliability of the statistical analysis and its guarantees. In this work, we combat the view that statistical techniques only provide weak guarantees by introducing a new algorithm for the analysis of timing measurements with strong, formal statistical guarantees, giving developers a reliable analysis tool. Specifically, our algorithm (1) is non-parametric, making minimal assumptions about the underlying distribution and thus overcoming limitations of classical tests like the t-test, (2) handles unknown data dependencies in measurements, (3) can estimate in advance how many samples are needed to detect a leak of a given size, and (4) allows the definition of a negligible leak threshold $Δ$, ensuring that acceptable non-exploitable leaks do not trigger false positives, without compromising statistical soundness. We demonstrate the necessity, effectiveness, and benefits of our approach on both synthetic benchmarks and real-world applications.

cs.CR↗

arXiv2025

Detecting relevant dependencies under measurement error with applications to the analysis of planetary system evolution

Exoplanets play an important role in understanding the mechanics of planetary system formation and orbital evolution. In this context the correlations of different parameters of the planets and their host star are useful guides in the search for explanatory mechanisms. Based on a reanalysis of the data set from \cite{figueria14} we study the as of now still poorly understood correlation between planetary surface gravity and stellar activity of Hot Jupiters. Unfortunately, data collection often suffers from measurement errors due to complicated and indirect measurement setups, rendering standard inference techniques unreliable. We present new methods to estimate and test for correlations in a deconvolution framework and thereby improve the state of the art analysis of the data in two directions. First, we are now able to account for additive measurement errors which facilitates reliable inference. Second we test for relevant changes, i.e. we are testing for correlations exceeding a certain threshold $Δ$. This reflects the fact that small nonzero correlations are to be expected for real life data almost always and that standard statistical tests will therefore always reject the null of no correlation given sufficient data. Our theory focuses on quantities that can be estimated by U-Statistics which contain a variety of correlation measures. We propose a bootstrap test and establish its theoretical validity. As a by product we also obtain confidence intervals. Applying our methods to the Hot Jupiter data set from \cite{figueria14}, we observe that taking into account the measurement errors yields smaller point estimates and the null of no relevant correlation is rejected only for very small $Δ$. This demonstrates the importance of considering the impact of measurement errors to avoid misleading conclusions from the resulting statistical analysis.

stat.ME↗

arXiv2024

SoK: Prudent Evaluation Practices for Fuzzing

Fuzzing has proven to be a highly effective approach to uncover software bugs over the past decade. After AFL popularized the groundbreaking concept of lightweight coverage feedback, the field of fuzzing has seen a vast amount of scientific work proposing new techniques, improving methodological aspects of existing strategies, or porting existing methods to new domains. All such work must demonstrate its merit by showing its applicability to a problem, measuring its performance, and often showing its superiority over existing works in a thorough, empirical evaluation. Yet, fuzzing is highly sensitive to its target, environment, and circumstances, e.g., randomness in the testing process. After all, relying on randomness is one of the core principles of fuzzing, governing many aspects of a fuzzer's behavior. Combined with the often highly difficult to control environment, the reproducibility of experiments is a crucial concern and requires a prudent evaluation setup. To address these threats to validity, several works, most notably Evaluating Fuzz Testing by Klees et al., have outlined how a carefully designed evaluation setup should be implemented, but it remains unknown to what extent their recommendations have been adopted in practice. In this work, we systematically analyze the evaluation of 150 fuzzing papers published at the top venues between 2018 and 2023. We study how existing guidelines are implemented and observe potential shortcomings and pitfalls. We find a surprising disregard of the existing guidelines regarding statistical tests and systematic errors in fuzzing evaluations. For example, when investigating reported bugs, ...

cs.SE↗

arXiv2020

Simultaneous inference for Berkson errors-in-variables regression under fixed design

In various applications of regression analysis, in addition to errors in the dependent observations also errors in the predictor variables play a substantial role and need to be incorporated in the statistical modeling process. In this paper we consider a nonparametric measurement error model of Berkson type with fixed design regressors and centered random errors, which is in contrast to much existing work in which the predictors are taken as random observations with random noise. Based on an estimator that takes the error in the predictor into account and on a suitable Gaussian approximation, we derive %uniform confidence statements for the function of interest. In particular, we provide finite sample bounds on the coverage error of uniform confidence bands, where we circumvent the use of extreme-value theory and rather rely on recent results on anti-concentration of Gaussian processes. In a simulation study we investigate the performance of the uniform confidence sets for finite samples.

math.ST↗

arXiv2019

The empirical process of residuals from an inverse regression

In this paper we investigate an indirect regression model characterized by the Radon transformation. This model is useful for recovery of medical images obtained by computed tomography scans. The indirect regression function is estimated using a series estimator motivated by a spectral cut-off technique. Further, we investigate the empirical process of residuals from this regression, and show that it satsifies a functional central limit theorem.

math.ST↗

arXiv2018

Goodness-of-fit testing the error distribution in multivariate indirect regression

We propose a goodness-of-fit test for the distribution of errors from a multivariate indirect regression model. The test statistic is based on the Khmaladze transformation of the empirical process of standardized residuals. This goodness-of-fit test is consistent at the root-n rate of convergence, and the test can maintain power against local alternatives converging to the null at a root-n rate.

stat.ME↗

arXiv2018

Regularization parameter selection in indirect regression by residual based bootstrap

Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a regularization parameter used in our indirect regression function estimator. The resulting methodology is based on a smooth bootstrap of the model residuals. A simulation study demonstrates the effectiveness of our approach.

stat.ME↗

arXiv2017

Risk Estimators for Choosing Regularization Parameters in Ill-Posed Problems - Properties and Limitations

This paper discusses the properties of certain risk estimators recently proposed to choose regularization parameters in ill-posed problems. A simple approach is Stein's unbiased risk estimator (SURE), which estimates the risk in the data space, while a recent modification (GSURE) estimates the risk in the space of the unknown variable. It seems intuitive that the latter is more appropriate for ill-posed problems, since the properties in the data space do not tell much about the quality of the reconstruction. We provide theoretical studies of both estimators for linear Tikhonov regularization in a finite dimensional setting and estimate the quality of the risk estimators, which also leads to asymptotic convergence results as the dimension of the problem tends to infinity. Unlike previous papers, who studied image processing problems with a very low degree of ill-posedness, we are interested in the behavior of the risk estimators for increasing ill-posedness. Interestingly, our theoretical results indicate that the quality of the GSURE risk can deteriorate asymptotically for ill-posed problems, which is confirmed by a detailed numerical study. The latter shows that in many cases the GSURE estimator leads to extremely small regularization parameters, which obviously cannot stabilize the reconstruction. Similar but less severe issues with respect to robustness also appear for the SURE estimator, which in comparison to the rather conservative discrepancy principle leads to the conclusion that regularization parameter choice based on unbiased risk estimation is not a reliable procedure for ill-posed problems. A similar numerical study for sparsity regularization demonstrates that the same issue appears in nonlinear variational regularization approaches.

math.ST↗

arXiv2016

Multiscale inference for multivariate deconvolution

In this paper we provide new methodology for inference of the geometric features of a multivariate density in deconvolution. Our approach is based on multiscale tests to detect significant directional derivatives of the unknown density at arbitrary points in arbitrary directions. The multiscale method is used to identify regions of monotonicity and to construct a general procedure for the detection of modes of the multivariate density. Moreover, as an important application a significance test for the presence of a local maximum at a pre-specified point is proposed. The performance of the new methods is investigated from a theoretical point of view and the finite sample properties are illustrated by means of a small simulation study.

stat.ME↗

arXiv2016

Uncertainty Limits on Solutions of Inverse Problems over Multiple Orders of Magnitude using Bootstrap Methods: An Astroparticle Physics Example

Astroparticle experiments such as IceCube or MAGIC require a deconvolution of their measured data with respect to the response function of the detector to provide the distributions of interest, e.g. energy spectra. In this paper, appropriate uncertainty limits that also allow to draw conclusions on the geometric shape of the underlying distribution are determined using bootstrap methods, which are frequently applied in statistical applications. Bootstrap is a collective term for resampling methods that can be employed to approximate unknown probability distributions or features thereof. A clear advantage of bootstrap methods is their wide range of applicability. For instance, they yield reliable results, even if the usual normality assumption is violated. The use, meaning and construction of uncertainty limits to any user-specific confidence level in the form of confidence intervals and levels are discussed. The precise algorithms for the implementation of these methods, applicable for any deconvolution algorithm, are given. The proposed methods are applied to Monte Carlo simulations to show their feasibility and their precision in comparison to the statistical uncertainties calculated with the deconvolution software TRUEE.

astro-ph.IM↗

arXiv2016

Multiscale inference for a multivariate density with applications to X-ray astronomy

In this paper we propose methods for inference of the geometric features of a multivariate density. Our approach uses multiscale tests for the monotonicity of the density at arbitrary points in arbitrary directions. In particular, a significance test for a mode at a specific point is constructed. Moreover, we develop multiscale methods for identifying regions of monotonicity and a general procedure for detecting the modes of a multivariate density. It is is shown that the latter method localizes the modes with an effectively optimal rate. The theoretical results are illustrated by means of a simulation study and a data example. The new method is applied to and motivated by the determination and verification of the position of high-energy sources from X-ray observations by the Swift satellite which is important for a multiwavelength analysis of objects such as Active Galactic Nuclei.

math.ST↗

arXiv2015

Confidence bands for multivariate and time dependent inverse regression models

Uniform asymptotic confidence bands for a multivariate regression function in an inverse regression model with a convolution-type operator are constructed. The results are derived using strong approximation methods and a limit theorem for the supremum of a stationary Gaussian field over an increasing system of sets. As a particular application, asymptotic confidence bands for a time dependent regression function $f_t(x)$ ($x\in \mathbb {R}^d,t\in \mathbb {R}$) in a convolution-type inverse regression model are obtained. Finally, we demonstrate the practical feasibility of our proposed methods in a simulation study and an application to the estimation of the luminosity profile of the elliptical galaxy NGC5017. To the best knowledge of the authors, the results presented in this paper are the first which provide uniform confidence bands for multivariate nonparametric function estimation in inverse problems.

math.ST↗

arXiv2013

Smooth backfitting in additive inverse regression

We consider the problem of estimating an additive regression function in an inverse regres- sion model with a convolution type operator. A smooth backfitting procedure is developed and asymptotic normality of the resulting estimator is established. Compared to other meth- ods for the estimation in additive models the new approach neither requires observations on a regular grid nor the estimation of the joint density of the predictor. It is also demonstrated by means of a simulation study that the backfitting estimator outperforms the marginal in- tegration method at least by a factor two with respect to the integrated mean squared error criterion.

stat.ME↗

arXiv2011

Improving PSF calibration in confocal microscopic imaging---estimating and exploiting bilateral symmetry

A method for estimating the axis of reflectional symmetry of an image $f(x,y)$ on the unit disc $D=\{(x,y):x^2+y^2\leq1\}$ is proposed, given that noisy data of $f(x,y)$ are observed on a discrete grid of edge width $Δ$. Our estimation procedure is based on minimizing over $β\in[0,π)$ the $L_2$ distance between empirical versions of $f$ and $τ_βf$, the image of $f$ after reflection at the axis along $(\cosβ,\sinβ)$. Here, $f$ and $τ_βf$ are estimated using truncated radial series of the Zernike type. The inherent symmetry properties of the Zernike functions result in a particularly simple estimation procedure for $β$. It is shown that the estimate $\hatβ$ converges at the parametric rate $Δ^{-1}$ for images $f$ of bounded variation. Further, we establish asymptotic normality of $\hatβ$ if $f$ is Lipschitz continuous. The method is applied to calibrating the point spread function (PSF) for the deconvolution of images from confocal microscopy. For various reasons the PSF characterizing the problem may not be rotationally invariant but rather only reflection symmetric with respect to two orthogonal axes. For an image of a bead acquired by a confocal laser scanning microscope (Leica TCS), these axes are estimated and corresponding confidence intervals are constructed. They turn out to be close to the coordinate axes of the imaging device. As cause for deviation from rotational invariance, this indicates some slight misalignment of the optical system or anisotropy of the immersion medium rather than some irregular shape of the bead. In an extensive simulation study, we show that using a symmetrized version of the observed PSF significantly improves the subsequent reconstruction process of the target image.

stat.AP↗

arXiv2008

The Milky Way Spiral Arm Pattern

A complete map of the 3D distribution of molecular (CO) gas was constructed using a realistic dynamical model of the gas flow in the barred potential of the Milky Way. The map shows two prominent spiral arms starting at the bar ends connecting smoothly to the 4-armed spiral pattern observed in the atomic hydrogen gas in the outer Galaxy. Unlike previous attempts, our new map uncovers the gas distribution in the bar region of the Galaxy and the far side of the disk. For the first time, we can follow spiral arms in gas as they pass behind the galactic centre.

astro-ph↗

arXiv2007

3D Distribution of Molecular Gas in the Barred Milky Way

We present a new model of the three-dimensional distribution of molecular gas in the Milky Way Galaxy, based on CO line data. Our analysis is based on a gas-flow simulation of the inner Galaxy using smoothed-particle hydrodynamics (SPH) using a realistic barred gravitional potential derived from the observed COBE/DIRBE near-IR light distribution. The gas model prescribes the gas orbits much better than a simple circular rotation model and is highly constrained by observations, but it cannot predict local details. In this study, we provide a 3D map of the observed molecular gas distribution using the velocity field from the SPH model. A comparison with studies of the Galactic Center region suggests that the main structures are reproduced but somewhat stretched along the line-of-sight, probably on account of limited resolution of the underlying SPH simulation. The gas model will be publicly available and may prove useful in a number of applications, among them the analysis of diffuse gamma-ray emission as measured with GLAST.

astro-ph↗

arXiv2003

Large-Scale Model of the Milky Way: Stellar Kinematics and Microlensing Event Timescale Distribution in the Galactic Bulge

We build a stellar-dynamical model of the Milky Way barred bulge and disk, using a newly implemented adaptive particle method. The underlying mass model has been previously shown to match the Galactic near-infrared surface brightness as well as gas-kinematic observations. Here we show that the new stellar-dynamical model also matches the observed stellar kinematics in several bulge fields, and that its distribution of microlensing event timescales reproduces the observed timescale distribution of the {\it MACHO} experiment with a reasonable stellar mass function. The model is therefore an excellent basis for further studies of the Milky Way. We also predict the observational consequences of this mass function for parallax shifted events.

astro-ph↗