UIUC

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Visible to the public From Measurements to Security Science: Data-Driven Approach

ABOUT THE PROJECT:

In security more than in other computing disciplines, professionals depend heavily on rapid analysis of voluminous streams of data gathered by a combination of network-, file-, and system-level monitors. The data are used both to maintain a constant vigil against attacks and compromises on a target system and to improve the monitoring itself.

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Visible to the public Enhancing Cyber Security Through Networks Resilient to Targeted Attacks

ABOUT THE PROJECT:

The scientific objective of this project is to discover statistical models that characterize network resiliency, and develop simulation tools to test whether an existing network is resilient. Our work will show how to place questions of network connectivity resilience on a firm statistical basis, ultimately allowing one to design networks to be more resilient, formally assess the resiliency of existing networks, and formally assess the changes to resiliency achieved as modifications are introduced.

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Visible to the public End-to-End Analysis of Side Channels

This project is exploring a framework for characterizing side channels that is based on an end-to-end analysis of the side channel process. As in covert channel analysis, we are using information-theoretic tools to identify the potential of a worst-case attack, rather than the success of a given ad hoc approach.

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Visible to the public Classification of Cyber-Physical System Adversaries

Cyber-Physical Systems (CPS) are vulnerable to elusive dynamics-aware attacks that subtly change local behaviors in ways that lead to large deviations in global behavior, and to system instability. The broad agenda for this project is to classify attacks on different classes of CPS based on detectability. In particular, we are identifying attacks that are impossible to detect in a given class of CPS (with reasonable resources), and we are developing detection algorithms for those that are possible.