Thomas H. Austin

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A key challenge in dynamic information flow analysis is handling <i>implicit flows</i>, where code conditional on a private variable updates a public variable x. The naive approach of upgrading x to private results in x being <i>partially leaked</i>, where its value contains private data but its label might remain public on an alternative execution (where(More)
It is important for applications to protect sensitive data. Even for simple confidentiality and integrity policies, it is often difficult for programmers to reason about how the policies should interact and how to enforce policies across the program. A promising approach is <i>policy-agnostic programming</i>, a model that allows the programmer to implement(More)
Previous research has shown that hidden Markov model (HMM) analysis is useful for detecting certain challenging classes of malware. In this research, we consider the related problem of malware classification based on HMMs. We train multiple HMMs on a variety of compilers and malware generators. More than 8,000 malware samples are then scored against these(More)
An application that fails to ensure information flow security may leak sensitive data such as passwords, credit card numbers, or medical records. News stories of such failures abound. Austin and Flanagan[2] introduce faceted values – values that present different behavior according to the privilege of the observer – as a dynamic approach to enforce(More)
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the(More)