Seongmin Lee

The leakage of secret information via a public channel is a critical privacy flaw in software systems. The more information is leaked per observation, the less time an attacker needs to learn the secret. Due to the size and complexity of the modern software, and because some empirical facts are not available to a formal analysis of the source code, researchers started investigating statistical methods using program executions as samples. However, current statistical methods require a high sample coverage. Ideally, the sample is large enough to contain every possible combination of secret {}times observable value to accurately reflect the joint distribution of {}langle\secret, observable{}rangle\. Otherwise, the information leakage is severely underestimated, which is problematic as it can lead to overconfidence in the security of an otherwise vulnerable program. In this paper, we introduce an improved estimator for information leakage and propose to use methods from applied statistics to improve our estimate of the joint distribution when sample coverage is low. The key idea is to reconstruct the joint distribution by casting our problem as a multinomial estimation problem in the absence of samples for all classes. We suggest two approaches and demonstrate the effectiveness of each approach on a set of benchmark subjects. We also propose novel refinement heuristics, which help to adjust the joint distribution and gain better estimation accuracy. Compared to existing statistical methods for information leakage estimation, our method can safely overestimate the mutual information and provide a more accurate estimate from a limited number of program executions.

The missing mass refers to the proportion of data points in an unknown population of classifier inputs that belong to classes not present in the classifier’s training data, which is assumed to be a random sample from that unknown population. We find that in expectation the missing mass is entirely determined by the number \f_k of classes that do appear in the training data the same number of times and an exponentially decaying error. While this is the first precise characterization of the expected missing mass in terms of the sample, the induced estimator suffers from an impractically high variance. However, our theory suggests a large search space of nearly unbiased estimators that can be searched effectively and efficiently. Hence, we cast distribution-free estimation as an optimization problem to find a distribution-specific estimator with a minimized mean-squared error (MSE), given only the sample. In our experiments, our search algorithm discovers estimators that have a substantially smaller MSE than the state-of-the-art Good-Turing estimator. This holds for over 93% of runs when there are at least as many samples as classes. Our estimators’ MSE is roughly 80% of the Good-Turing estimator’s.

Discovering how program components affect one another plays a fundamental role in aiding engineers comprehend and maintain a software system. Despite the fact that the degree to which one program component depends upon another can vary in strength, traditional dependence analysis typically ignores such nuance. To account for this nuance in dependence-based analysis, we propose Causal Program Dependence Analysis (CPDA), a framework based on causal inference that captures the degree (or strength) of the dependence between program elements. For a given program, CPDA intervenes in the program execution to observe changes in value at selected points in the source code. It observes the association between program elements by constructing and executing modified versions of a program (requiring only light-weight parsing rather than sophisticated static analysis). CPDA applies causal inference to the observed changes to identify and estimate the strength of the dependence relations between program elements. We explore the advantages of CPDA’s quantified dependence by presenting results for several applications. Our further qualitative evaluation demonstrates 1) that observing different levels of dependence facilitates grouping various functional aspects found in a program and 2) how focusing on the relative strength of the dependences for a particular program element provides a detailed context for that element. Furthermore, a case study that applies CPDA to debugging illustrates how it can improve engineer productivity.

A fuzzer can literally run forever. However, as more resources are spent, the coverage rate continuously drops, and the utility of the fuzzer declines. To tackle this coverage-resource tradeoff, we could introduce a policy to stop a campaign whenever the coverage rate drops below a certain threshold value, say 10 new branches covered per 15 minutes. During the campaign, can we predict the coverage rate at some point in the future? If so, how well can we predict the future coverage rate as the prediction horizon or the current campaign length increases? How can we tackle the statistical challenge of adaptive bias, which is inherent in greybox fuzzing (i.e., samples are not independent and identically distributed)?In this paper, we i) evaluate existing statistical techniques to predict the coverage rate U(t0 + k) at any time t0 in the campaign after a period of k units of time in the future and ii) develop a new extrapolation methodology that tackles the adaptive bias. We propose to efficiently simulate a large number of blackbox campaigns from the collected coverage data, estimate the coverage rate for each of these blackbox campaigns and conduct a simple regression to extrapolate the coverage rate for the greybox campaign.Our empirical evaluation using the Fuzztastic fuzzer benchmark demonstrates that our extrapolation methodology exhibits at least one order of magnitude lower error compared to the existing benchmark for 4 out of 5 experimental subjects we investigated. Notably, compared to the existing extrapolation methodology, our extrapolator excels in making long-term predictions, such as those extending up to three times the length of the current campaign.

Given a target program state (or statement) s, what is the probability that an input reaches s? This is the quantitative reachability analysis problem. For instance, quantitative reachability analysis can be used to approximate the reliability of a program (where s is a bad state). Traditionally, quantitative reachability analysis is solved as a model counting problem for a formal constraint that represents the (approximate) reachability of s along paths in the program, i.e., probabilistic reachability analysis. However, in preliminary experiments, we failed to run state-of-the-art probabilistic reachability analysis on reasonably large programs. In this paper, we explore statistical methods to estimate reachability probability. An advantage of statistical reasoning is that the size and composition of the program are insubstantial as long as the program can be executed. We are particularly interested in the error compared to the state-of-the-art probabilistic reachability analysis. We realize that existing estimators do not exploit the inherent structure of the program and develop structure-aware estimators to further reduce the estimation error given the same number of samples. Our empirical evaluation on previous and new benchmark programs shows that (i) our statistical reachability analysis outperforms state-of-the-art probabilistic reachability analysis tools in terms of accuracy, efficiency, and scalability, and (ii) our structure-aware estimators further outperform (blackbox) estimators that do not exploit the inherent program structure. We also identify multiple program properties that limit the applicability of the existing probabilistic analysis techniques.