CMU SoS Lablet Quarterly Executive Summary - April 2021

A. Fundamental Research
High level report of result or partial result that helped move security science forward-- In most cases it should point to a "hard problem". These are the most important research accomplishments of the Lablet in the previous quarter.

Jonathan Aldrich

Obsidian: A Language for Secure-by-Construction Blockchain Programs

Blockchains have been proposed to support transactions on distributed, shared state, but hackers have exploited security vulnerabilities in existing programs. We applied user-centered design in the creation of Obsidian, a new language that uses typestate and linearity to support stronger safety guarantees than current approaches for programming blockchain systems.

Lujo Bauer

Securing Safety-Critical Machine Learning Algorithms

Our upcoming AsiaCCS paper extends our previous arXiv preprint with some new results: we had previously shown that malware binaries could often be transformed so that they evaded correct classification by anti-virus programs (i.e., they would be incorrectly classified as benign). Leveraging an expanded experimental infrastructure, we more recently showed that such attacks can ultimately succeed even when attempting to transform binaries that initially appear resistant to attack. Specifically, we recognized that previous attacks are sufficiently stochastic that even when they usually fail, a determined adversary who attempts enough attacks will eventually succeed with high probability.

Lorrie Cranor

Characterizing user behavior and anticipating its effects on computer security with a Security Behavior Observatory

The SBO addresses the hard problem of “Understanding and Accounting for Human Behavior” by collecting data directly from people’s own home computers, thereby capturing people’s computing behavior “in the wild.”

David Garlan

Model-Based Explanation For Human-in-the-Loop Security

Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. Game theory approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is translated into a Bayesian multi-player game, where each component is explicitly modeled as an independent player while security attacks are encoded as variant types for the components. The optimal defensive strategy for the system is dynamically computed by solving the pure equilibrium (i.e., adaptation response) to achieve the best possible system utility, improving the resiliency of the system against security attacks. 

Joshua Sunshine

Security Science Research Experience for Undergraduates

No update for this quarter.