This project involves the application of argumentation techniques for reasoning about policies, and security decisions in particular. Specifically, we are producing a security-enhanced argumentation framework that (a) provides not only inferences to draw but also actions to take; (b) considers multiparty argumentation; (c) measures the mass of evidence on both attacking and supporting arguments in order to derive a defensible conclusion with confidence; and (d) develops suitable critical questions as the basis for argumentation. The end result would be a tool that helps system administrators and other stakeholders capture and reason about their rationales as a way of ensuring that they make sound decisions regarding policies.
TEAM
PIs: Munindar P. Singh, Simon D. Parsons (CUNU)
Student: Nirav Ajmeri
Dr. Munindar P. Singh is Alumni Distinguished Graduate Professor in the Department of Computer Science at North Carolina State University. He is a co-director of the DoD-sponsored Science of Security Lablet at NCSU, one of six nationwide. Munindar’s research interests include computational aspects of sociotechnical systems, especially as a basis for addressing challenges such as ethics, safety, resilience, trust, and privacy in connection with AI and multiagent systems.
Munindar is a Fellow of AAAI (Association for the Advancement of Artificial Intelligence), AAAS (American Association for the Advancement of Science), ACM (Association for Computing Machinery), and IEEE (Institute of Electrical and Electronics Engineers), and was elected a foreign member of Academia Europaea (honoris causa). He has won the ACM/SIGAI Autonomous Agents Research Award, the IEEE TCSVC Research Innovation Award, and the IFAAMAS Influential Paper Award. He won NC State University’s Outstanding Graduate Faculty Mentor Award as well as the Outstanding Research Achievement Award (twice). He was selected as an Alumni Distinguished Graduate Professor and elected to NCSU’s Research Leadership Academy.
Munindar was the editor-in-chief of the ACM Transactions on Internet Technology from 2012 to 2018 and the editor-in-chief of IEEE Internet Computing from 1999 to 2002. His current editorial service includes IEEE Internet Computing, Journal of Artificial Intelligence Research, Journal of Autonomous Agents and Multiagent Systems, IEEE Transactions on Services Computing, and ACM Transactions on Intelligent Systems and Technology. Munindar served on the founding board of directors of IFAAMAS, the International Foundation for Autonomous Agents and MultiAgent Systems. He previously served on the editorial board of the Journal of Web Semantics. He also served on the founding steering committee for the IEEE Transactions on Mobile Computing. Munindar was a general co-chair for the 2005 International Conference on Autonomous Agents and MultiAgent Systems and the 2016 International Conference on Service-Oriented Computing.
Munindar’s research has been recognized with awards and sponsorship by (alphabetically) Army Research Lab, Army Research Office, Cisco Systems, Consortium for Ocean Leadership, DARPA, Department of Defense, Ericsson, Facebook, IBM, Intel, National Science Foundation, and Xerox.
Twenty-nine students have received Ph.D. degrees and thirty-nine students MS degrees under Munindar’s direction.
We are studying how to harness human visual perception in information display, with a specific focus on ways to combine layers of data in a common, well-understood display framework. Our visualization techniques are designed to present data in ways that are efficient and effective, allowing an analyst to explore large amounts of data rapidly and accurately.
TEAM
PI: Christopher G. Healey
Student: Terry Rogers
TEAM
PI: Ting Yu
Students: Xi Gong, Entong Shen
This project aims to discover general theory to explain what cues security experts use to decide when to apply security requirements and how to present those cues in the form of security patterns to novice designers in a way that yields improved security designs.
TEAM
PIs: Travis Breaux (CMU), Laurie Williams, & Jianwei Niu (CMU)
Student: Maria Riaz
Dr. Breaux is the Director of the CMU Requirements Engineering Lab, where his research program investigates how to specify and design software to comply with policy and law in a trustworthy, reliable manner. His work historically concerned the empirical extraction of legal requirements from policies and law, and has recently studied how to use formal specifications to reason about privacy policy compliance, how to measure and reason over ambiguous and vague policies, and how security and privacy experts and novices estimate the risk of system designs.
To learn more, read about his ongoing research projects or contact him.
Software security metrics are commonly considered as one critical component of science of security. We propose to investigate existing metrics and new security metrics to predict which code locations are likely to contain vulnerabilities. In particular, we will investigate security metrics to take into account of comprehensive factors such as software internal attributes, developers who develop the software, attackers who attack the software, and users who use the software. The project also investigates metrics to evaluate firewall security objectively. The developed metrics including risk, usability and cost are used to automate the creation of security architecture and configurations.
TEAM
PIs: Tao Xie, Laurie Williams, & Ehab S. Al-Shaer (UNC-Charlotte)
Students: Jason King, Rahul Pandita, & Mahamed Alsaleh
Phishing has become a serious threat in the past several years, and combating it is increasingly important. Why do certain people get phished and others do not? In this project, we aim to identify the factors that cause people to be susceptible and resistant to phishing attacks. In doing so, we aim to deploy adaptive anti-phishing measures.
The objective of this project is to design empirical privacy metrics that are independent of existing privacy models to naturally reflect the privacy offered by anonymization. We propose to model privacy attacks as an inference process and develop an inference framework over anonymized data (independent of specific privacy objects and techniques for data anonymization) where machine-learning techniques can be integrated to implement various attacks. The privacy metrics is then defined as the accuracy of the inference of individuals’ sensitive attributes. Data utility is modeled as a data aggregation process and thus can be measured in terms of accuracy of aggregate query answering. Our hypothesis is that, given the above empirical privacy and utility metrics, differential privacy based anonymization techniques offers a better privacy/utility tradeoff, when appropriate parameters are set. In particular, it is possible to improve utility greatly while imposing limited impact on privacy.
TEAM
PIs: Chris Mayhorn & Emerson Murphy-Hill
Students: Kyung Wha Hong & Chris Kelly
This project investigates the hard problem of resilient architectures from the standpoint of enabling new potential for incorporating privilege separation into computing systems. However, privilege separation alone is insufficient to achieve strong security guarantees. It must also include a security policy for separated components without impacting the functional requirements of the system. The general hypothesis of this project is that, legacy computing systems contain emergent properties that allow automatic software partitioning for privilege separation capable of supporting practical least privilege security policies.
Team
PIs: William Enck & Xiaohui (Helen) Gu
Students: Adwait Nadkami, Tsun-Hsuan (Anson) Ho, Ashwin Shashidharan
Welcome to C3E2013!
Now in our fifth year, we'll gather again at West Point later this October to continue our exploration of work begun during the April 2013 mid-year event by focusing in two areas:
Drawing upon past C3E themes of predictive analytics, visualization, decision-making and others, we’ll take C3E into its fifth year by focusing in two key areas: