Engineering Commons^™

The Nature Of Ephemeral Secrets In Reverse Engineering Tasks, Antonio Miguel Espinoza

Computer Science ETDs

Reverse engineering is typically carried out on static binary objects, such as files or compiled programs. Often the goal of reverse engineering is to extract a secret that is ephemeral and only exists while the system is running. Automation and dynamic analysis enable reverse engineers to extract ephemeral secrets from dynamic systems, obviating the need for analyzing static artifacts such as executable binaries.

I support this thesis through four automated reverse engineering efforts: (1) named entity extraction to track Chinese Internet censorship based on keywords; (2) dynamic information flow tracking to locate secret keys in memory for a live program; …

Criticality Assessments For Improving Algorithmic Robustness, Thomas B. Jones

Computer Science ETDs

Though computational models typically assume all program steps execute flawlessly, that does not imply all steps are equally important if a failure should occur. In the "Constrained Reliability Allocation" problem, sufficient resources are guaranteed for operations that prompt eventual program termination on failure, but those operations that only cause output errors are given a limited budget of some vital resource, insufficient to ensure correct operation for each of them.

In this dissertation, I present a novel representation of failures based on a combination of their timing and location combined with criticality assessments---a method used to predict the behavior of systems …

Data-Driven Uncertainty Quantification Interpretation With High Density Regions, Matthew Gregor Peterson

Computer Science ETDs

In a time when data is being constantly generated by phones, vehicles, sensor net- works, social media, etc. detecting anomalies with in the data can be very crucial. In cases where we know little prior knowledge about the data, it becomes difficult to extract uncertainty about our results. In this thesis, we will propose a framework in which we can extract uncertainty distributions from data-driven modeling prob- lems. We will show some concrete examples of how to apply framework and provide some insight into what the uncertainty distributions are telling us using High Density Regions (HDRs).

Automated Development Of Semantic Data Models Using Scientific Publications, Martha O. Perez-Arriaga

Computer Science ETDs

The traditional methods for analyzing information in digital documents have evolved with the ever-increasing volume of data. Some challenges in analyzing scientific publications include the lack of a unified vocabulary and a defined context, different standards and formats in presenting information, various types of data, and diverse areas of knowledge. These challenges hinder detecting, understanding, comparing, sharing, and querying information rapidly.

I design a dynamic conceptual data model with common elements in publications from any domain, such as context, metadata, and tables. To enhance the models, I use related definitions contained in ontologies and the Internet. Therefore, this dissertation generates …