This project seeks to aid security analysts in identifying and protecting against accidental and malicious actions by users or software through automated reasoning on unified representations of user expectations and software implementation to identify misuses sensitive to usage and machine context.

Systems that are technically secure may still be exploited if users behave in unsafe ways. Most studies of user behavior are in controlled laboratory settings or in large-scale between-subjects measurements in the field. Both methods have shortcomings: lab experiments are not in natural environments and therefore may not accurately capture real world behaviors (i.e., low ecological validity), whereas large-scale measurement studies do not allow the researchers to probe user intent or gather explanatory data for observed behaviors, and they offer limited control for confounding factors. The SBO addresses the gap through a panel of participants consenting to our observing their daily computing behavior in situ, so we can understand what constitutes “insecure” behavior. We use the security behavior observatory to attempt to answer a number of research questions, including 1) What are risk indicators of a user’s propensity to be infected by malware?  2) Why do victims fail to update vulnerable software in a timely manner? 3) How can user behavior be modeled with respect to security and privacy “in the wild”?

Typically, securing software is the responsibility of the software developer. The customer or end-user of the software does not control or direct the steps taken by the developer to employ best practice coding styles or mechanisms to ensure software security and robustness. Current systems and tools also do not provide the end-user with an ability to determine the level of security in the software they use. At the same time, any flaw or security vulnerabilities ultimately affect the end-user of the software. Therefore, our overall project aim is to provide greater control to the end-user to actively assess and secure the software they use.

Our project goal is to develop a high-performance framework for client-side security assessment and enforcement for binary software. Our research is developing new tools and techniques to: (a) assess the security level of binary executables, and (b) enhance the security level of binary software, when and as desired by the user to protect the binary against various classes of security issues. Our approach combines static and dynamic techniques to achieve efficiency, effectiveness, and accuracy.