A key concern in security is identifying differences between human users and "bot" programs that emulate humans. Users with malicious intent will often utilize wide-spread computational attacks in order to exploit systems and gain control. Conventional detection techniques can be grouped into two broad categories: human observational proofs (HOPs) and human interactive proofs (HIPs). The key distinguishing feature of these techniques is the degree to which human participants are actively engaged with the "proof." HIPs require explicit action on the part of users to establish their identity (or at least distinguish them from bots). On the other hand, HOPs are passive. They examine the ways in which users complete the tasks they would normally be completing and look for patterns that are indicative of humans vs. bots. HIPs and HOPs have significant limitations. HOPs are susceptible to imitation attacks, in which bots carry out scripted actions designed to look like human behavior. HIPs, on the other hand, tend to be more secure because they require explicit action from a user to complete a dynamically generated test. Because humans have to expend cognitive effort in order pass HIPs, they can be disruptive or reduce productivity. We are developing the knowledge and techniques to enable "Human Subtlety Proofs" (HSPs) that blend the stronger security characteristics of HIPs with the unobtrusiveness of HOPs. HSPs will improve security by providing a new avenue for actively securing systems from non-human users.
Leveraging the Effects of Cognitive Function on Input Device Analytics to Improve Security