Fuzzing is an effective software testing technique to find bugs. Given the size and complexity of real-world applications, modern fuzzers tend to be either scalable, but not effective in exploring bugs that lie deeper in the execution, or capable of penetrating deeper in the application, but not scalable. In this paper, we present an application-aware evolutionary fuzzing strategy that does not require any prior knowledge of the application or input format. In order to maximize coverage and explore deeper paths, we leverage control-and data-flow features based on static and dynamic analysis to infer fundamental properties of the application. This enables much faster generation of interesting inputs compared to an application-agnostic approach. We implement our fuzzing strategy in VUzzer and evaluate it on three different datasets: DARPA Grand Challenge binaries (CGC), a set of real-world applications (binary input parsers), and the recently released LAVA dataset. On all of these datasets, VUzzer yields significantly better results than state-of-the-art fuzzers, by quickly finding several existing and new bugs. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.
Simultaneous stochastic optimization of mining complexes aims to optimize its different components in a single optimization model under grade, geometallurgical and material type uncertainty. The single optimization model capitalizes on synergies between the different components and the quantified variability and uncertainty of the materials mined, to better meet production targets while maximizing the net present value (NPV) of a mining complex. Integrating uncertainty and decisions about geometallurgical aspects of materials in the optimization model assists in achieving higher and more stable throughput with comminution circuits. This paper introduces an approach to integrate uncertainty and decisions about two non-additive geometallurgical properties, semi-autogenous power index and bond work index in the simultaneous stochastic optimization model. An application of the proposed approach at a large copper-gold mining complex indicates higher chances of meeting the different production targets, substantial increase in metal production and a 19.3% increase in NPV compared to the conventional plan.
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