It is widely accepted that wireless channels decorrelate fast over space, and half a wavelength is the key distance metric used in existing wireless physical layer security mechanisms for security assurance. We believe that this channel correlation model is incorrect in general: it leads to wrong hypothesis about the inference capability of a passive adversary and results in false sense of security, which will expose the legitimate systems to severe threats with little awareness. In this project, we focus on establishing correct modeling of channel correlation in wireless environments of interest, and properly evaluating the safety distance metric of existing and emerging wireless security mechanisms, as well as cyber-physical systems employing these security mechanisms. Upon successful completion of the project, the expected outcome will allow us to accurately determine key system parameters (e.g., the security zone for secrete key establishment from wireless channels) and confidently assess the security assurance in wireless security mechanisms. More importantly, the results will correct the previous misconception of channel de-correlation, and help security researchers develop new wireless security mechanisms based on a proven scientific foundation.

TEAM

PIs: Huaiyu Dai, Peng Ning
Student: Xiaofan He

It is already true that the majority of users' computing experience is a mobile one. Unfortunately that mobile experience is also more risky: users are often multitasking, hurrying or uncomfortable, leading them to make poor decisions. Our goal is to use mobile sensors to predict when users are distracted in these ways, and likely to behave insecurely. We will study this possibility in a series of lab and field experiments.

TEAM

PIs: Benjamin Watson, Will Enck, Anne McLaughlin, Michael Rappa

Programmers interact with a variety of tools that help them do their jobs, from "undo" to FindBugs' security warnings to entire development environments. However, programmers typically know about only a small subset of tools that are available, even when many of those tools might be valuable to them. In this project, we investigate how and why software developers find out about -- and don't find out about -- software security tools. The goal of the project is to help developers use more relevant security tools, more often.

TEAM

PI: Emerson Murphy-Hill
Student: Jim Witschey

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.

TEAM

PIs: David Roberts, Robert St. Amant
Students: Titus Barik, Arpan Chakraborty, Brent Harrison

This project seeks to develop a deeper understanding of trust than is supported by current methods, which largely disregard the underlying relationships based on which people trust or not trust each other. Accordingly, we begin from the notion of what we term normative relationships—or norms for short—directed from one principal to another. An example of a normative relationship is a commitment: is the first principal committed to doing something for the second principal? (The other main types of normative relationships are authorizations, prohibitions, powers, and sanctions.) Our broad research hypothesis is that trust can be modeled in terms of the relevant norms being satisfied or violated. To demonstrate the viability of this approach, we are mining commitments from emails (drawn from the well-known Enron dataset) and using them to assess trust. Preliminary results indicate that our methods can effectively estimate the trust-judgment profiles of human subjects.

TEAM

PI: Munindar Singh
Student: Anup Kalia

The eventual goal of our research is to develop a principled design for comprehensively mitigating access-driven timing channels in modern compute clouds, particularly of the "infrastructure as a service" (IaaS) variety. This type of cloud permits the cloud customer to deploy arbitrary guest virtual machines (VMs) to the cloud. The security of the cloud-resident guest VMs depends on the virtual machine monitor (VMM), e.g., Xen, to adequately isolate guest VMs from one another. While modern VMMs are designed to logically isolate guest VMs, there remains the possibility of timing "side channels" that permit one guest VM to learn information about another guest VM simply by observing features that reflect the others' effects on the hardware platform. Such attacks are sometimes referred to as "access-driven" timing attacks.

TEAM

PI: Michael Reiter (UNC)
Student: Yinqian Zhang, Peng Li

In this project, our focus is on understanding a class of security systems in analytical terms at a certain level of abstraction.  Specifically, the systems we intend to look at are (I) multipath routing (for increasing reliability), (ii) dynamic firewalls.  For multipath routing, the threat scenario is jamming – the nodes that are disabled due to the jamming take the place of compromised components in that they fail to perform their proper function.  The multipath and diverse path mechanisms are intended to allow the system to perform its overall function (critical message delivery) despite this.  The project will focus on quantifying and bounding this ability to function redundantly.  For the firewall, the compromise consists of an attacker guessing at the firewall rules and being able to circumvent them.  The system is designed to withstand this by dynamically changing the ruleset to be applied over time. Our project will focus on quantifying or characterizing this ability.

TEAM

PIs: Rudra Dutta, Meeko Oishi (UNM-Albuquerque)
Student Trisha Biswas

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.

TEAM

PI: David Roberts
Student: Titus Barik

Human interaction is an integral part of any system. Users have daily interactions with a system and make many decisions that affect the overall state of security. The fallibility of users has been shown but there is little research focused on the fundamental principles to optimize the usability of security mechanisms. We plan to develop a framework to design, develop and evaluate user interaction in a security context. We will (a) examine current security mechanisms and develop basic principles which can influence security interface design; (b) introduce new paradigms for security interfaces that utilize those principles; (c) design new human-centric security mechanisms for several problem areas to illustrate the paradigms; and (d) conduct repeatable human subject experiments to evaluate and refine the principles and paradigms developed in this research.

TEAM

PIs: Ting Yu, Ninghui Li (Purdue), Robert Proctor (Purdue)
Student: Zach Jorgensen

In this project, we aim to systematize the knowledge base about existing mobile malware (especially on Android) and quantify their threats so that we can develop principled solutions to provably determine their presence or absence in existing marketplaces. The hypothesis is that there exist certain fundamental commonalities among existing mobile malware. Accordingly, we propose a mobile malware genome project called MalGenome with a large collection of mobile malware samples.  Based on the collection, we can then precisely systematize their fundamental commonalities (in terms of violated security properties and behaviors) and quantify their possible threats on mobile devices.  After that, we can develop principled solutions to scalably and accurately determine their presence in existing marketplaces. Moreover, to predict or uncover unknown (or zero-day) malware, we can also leverage the systematized knowledge base to generate an empirical prediction model. This model can also be rigorously and thoroughly evaluated for its repeatability and accuracy.

TEAM

PI: Xuxian Jiang
Student: Yajin Zhou