一句话：提出结合模糊测试与符号执行的可扩展差分程序分析框架，用于回归分析与侧信道分析。
差分程序分析旨在识别程序的行为差异，可分为两类：同一输入下两个程序版本之间的差异（回归分析），以及同一程序在两个不同输入下的差异（如侧信道分析）。现有方法大多依赖单一技术，因而受制于其固有弱点，如可扩展性差或精度不足。本文（研究提案）提出将模糊测试与符号执行这两种互补的强技术相结合，以应对上述两类子问题，并为真实应用提供可扩展的解决方案。所提方法将构建在 AFL 与 Symbolic PathFinder 等先进工具之上，并与现有工作进行对比评估，以验证其在可扩展性与精度方面的优势。
Differential program analysis means to identify the behavioral divergences in one or multiple programs, and it can be classified into two categories: identify the behavioral divergences (1) between two program versions for the same input (aka regression analysis), and (2) for the same program with two different inputs (e.g, side-channel analysis). Most of the existent approaches for both subproblems try to solve it with single techniques, which suffer from its weaknesses like scalability issues or imprecision. This research proposes to combine two very strong techniques, namely fuzzing and symbolic execution to tackle these problems and provide scalable solutions for real-world applications. The proposed approaches will be implemented on top of state-of-the-art tools like AFL and Symbolic PathFinder to evaluate them against existent work.
DOI 原文 ·
@inproceedings{paperbot4795,
title = {Differential program analysis with fuzzing and symbolic execution},
author = {Yannic Noller},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3241537}
}