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Forget, Alain, Komanduri, Saranga, Acquisti, Alessandro, Christin, Nicolas, Cranor, Lorrie, Telang, Rahul.  2014.  Building the Security Behavior Observatory: An Infrastructure for Long-term Monitoring of Client Machines. IEEE Symposium and Bootcamp on the Science of Security (HotSoS) 2014.

We present an architecture for the Security Behavior Observatory
(SBO), a client-server infrastructure designed to
collect a wide array of data on user and computer behavior
from hundreds of participants over several years. The SBO
infrastructure had to be carefully designed to fulfill several
requirements. First, the SBO must scale with the desired
length, breadth, and depth of data collection. Second, we
must take extraordinary care to ensure the security of the
collected data, which will inevitably include intimate participant
behavioral data. Third, the SBO must serve our
research interests, which will inevitably change as collected
data is analyzed and interpreted. This short paper summarizes
some of our design and implementation benefits and
discusses a few hurdles and trade-offs to consider when designing
such a data collection system.

Alain Forget, Saranga Komanduri, Alessandro Acquisti, Nicolas Christin, Lorrie Cranor, Rahul Telang.  2014.  Building the security behavior observatory: an infrastructure for long-term monitoring of client machines. HotSoS '14 Proceedings of the 2014 Symposium and Bootcamp on the Science of Security.

We present an architecture for the Security Behavior Observatory (SBO), a client-server infrastructure designed to collect a wide array of data on user and computer behavior from hundreds of participants over several years. The SBO infrastructure had to be carefully designed to fulfill several requirements. First, the SBO must scale with the desired length, breadth, and depth of data collection. Second, we must take extraordinary care to ensure the security of the collected data, which will inevitably include intimate participant behavioral data. Third, the SBO must serve our research interests, which will inevitably change as collected data is analyzed and interpreted. This short paper summarizes some of our design and implementation benefits and discusses a few hurdles and trade-offs to consider when designing such a data collection system.

Luis Caires, Jorge Perez, Frank Pfenning, Bernardo Toninho.  2013.  Behavioral Polymorphism and Parametricity in Session-Based Communication. European Symposium on Programming 2013. 7792:330-349.

We investigate a notion of behavioral genericity in the context of session type disciplines. To this end, we develop a logically motivated theory of parametric polymorphism, reminiscent of the Girard-Reynolds polymorphic λ-calculus, but casted in the setting of concurrent processes. In our theory, polymorphism accounts for the exchange of abstract communication protocols and dynamic instantiation of heterogeneous interfaces, as opposed to the exchange of data types and dynamic instantiation of individual message types. Our polymorphic session-typed process language satisfies strong forms of type preservation and global progress, is strongly normalizing, and enjoys a relational parametricity principle. Combined, our results confer strong correctness guarantees for communicating systems. In particular, parametricity is key to derive non-trivial results about internal protocol independence, a concurrent analogous of representation independence, and non-interference properties of modular, distributed systems.

Serge Egelman, Marian Harbach, Eyal Peer.  2016.  Behavior Ever Follows Intention? A Validation of the Security Behavior Intentions Scale (SeBIS) CHI '16 Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. :5257-5261.

The Security Behavior Intentions Scale (SeBIS) measures the computer security attitudes of end-users. Because intentions are a prerequisite for planned behavior, the scale could therefore be useful for predicting users' computer security behaviors. We performed three experiments to identify correlations between each of SeBIS's four sub-scales and relevant computer security behaviors. We found that testing high on the awareness sub-scale correlated with correctly identifying a phishing website; testing high on the passwords sub-scale correlated with creating passwords that could not be quickly cracked; testing high on the updating sub-scale correlated with applying software updates; and testing high on the securement sub-scale correlated with smartphone lock screen usage (e.g., PINs). Our results indicate that SeBIS predicts certain computer security behaviors and that it is a reliable and valid tool that should be used in future research.