Search Projects: 2012

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Visible to the public A Language and Framework for Development of Secure Mobile Applications

Mobile applications are a critical emerging segment of the software industry, and security for web-based mobile applications is of increasing concern. We hypothesize that many of the most important security vulnerabilities in web-based mobile applications are a consequence of expressing programs at a low level of abstraction, in which important security properties are implicit and only indirectly related to code. In order to test this hypothesis, we are building a system for expressing web-based mobile applications at a higher level of abstraction, in which security properties a

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Visible to the public Trust from Explicit Evidence: Integrating Digital Signatures and Formal Proofs

ABOUT THE PROJECT:

This project is developing a common logical framework that will account for two principal sources of trust in software: digital signatures and explicit proof. The framework will allow us to rigorously specify, enforce, and analyze security policies that rely on multiple modes and sources of trust. Based on earlier work by the PI and collaborators, the framework is being cast as a modal type theory that comes equipped with a notation for programs and proofs.

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Visible to the public Scalable Methods for Security Against Distributed Attacks

ABOUT THE PROJECT:

This project is developing methods for resilient, efficient recognition of distributed attacks on clouds, data warehouses, clusters, and other massively parallel systems. Such attacks cannot usually be detected through local monitoring alone. Specifically, we are developing a probabilistic distributed temporal logic for characterizing such attacks and methods of verifying formulas in such a logic. The novel approach of combining probabilistic, distributed, and temporal operators will yield a new representation for system properties.

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Visible to the public The Science of Summarizing Systems: Generating Security Properties Using Data Mining and Formal Analysis

ABOUT THE PROJECT:

In this project, we are using our invariant generation methodologies for security focused applications, like generation of invariants for a new application from the Android app store. We are investigating the science behind re-engineering a system through iterative invariant generation. We are also studying the connections between the machine-learning model used by the data mining and the finite state machine model of the program that is actually learned.

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Visible to the public Secure Platforms via Stochastic Computing

ABOUT THE PROJECT:

The criticality of the information protection and assurance (IPA) problem has understandably sparked rich intellectual and material investment into finding a solution. Several efforts have centered on understanding, identifying, tolerating, and patching security vulnerabilities at different levels of the electronic system stack for various security attack models. Most of these approaches tend to fall into the "sand-boxing" category, whereby unusual events are sequestered until their potential impacts are identified.

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Visible to the public Theoretical Foundations of Threat Assessment by Inverse Optimal Control

ABOUT THE PROJECT:

Starting from the premise that cyber attacks have quantifiable objectives, our long-term goal is to develop computational tools that quickly recover the objective associated with any given attack by observing its effects. Knowledge of this objective is critical to threat assessment. It allows us to understand the scope of an attack, to predict how the attack will proceed, and to respond in a systematic and targeted way.