Amos Azaria

TL;DR: The most recent DARPA Challenge as mentioned in this paper focused on identifying influence bots on a specific topic within Twitter, and three top-ranked teams were identified by the DARPA Social Media in Strategic Communications program.

TL;DR: There is a need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussions on sites like Twitter and Facebook - before they get too influential.

TL;DR: SU-BRQR, a novel integration of human behavior model with the subjective utility function, significantly outperforms both MATCH and its improvements and is the first to present experimental results with security intelligence experts, and finds that even though the experts are more rational than the Amazon Turk workers, SU- BRQR still outperforms an approach assuming perfect rationality.

TL;DR: The behavioral analysis of insider threat (BAIT) framework is presented, and bootstrapping algorithms that learn from highly imbalanced data, mostly unlabeled, and almost no history of user behavior from an insider threat perspective are developed and evaluated.

TL;DR: This lab study suggests that users with little or no programming knowledge can successfully automate smartphone tasks using SUGILITE.