Current Research Projects

Research in IST cuts across traditional boundaries to drive interdisciplinary discovery and innovation. Our research is sponsored by a variety of national and international agencies, and we collaborate with diverse groups of scholars within and beyond Penn State. Explore our funded projects to see how IST's transformative research is addressing the world's most complex problems at the intersection of information, technology, and society.

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Researcher: Dinghao Wu
Sponsoring Agency: Office of Naval Research
We are considering a radically different approach to binary reverse engineering tools by placing the recompilability as the first and topmost goal. We will further develop our preliminary study on Reassembleable Disassembling, with the similar design goal to preserve the recompilability while lifting the code to higher level languages or intermediate representations.   Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Dinghao Wu
Sponsoring Agency: National Science Foundation
This project tackles the challenge of binary code analysis by proposing several advanced methods that combine techniques from behavior and semantics perspectives. The proposed methods leverage formal program semantics, symbolic execution, automated constraint solving, and algorithmic memorization of code semantics that form solid foundations with rigorous resilience properties to latest attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Shomir Wilson
Sponsoring Agency: National Science Foundation
This multi-disciplinary project aims to develop novel technology that will enable people to regain a sense of control by enabling them to simply ask questions about the privacy issues that matter to them rather than requiring them to read long, one-size-fits all privacy policies. This multi-disciplinary project aims to re-invent notice and choice, moving from long and hard-to-understand notices to interactive privacy dialogues with users. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: National Science Foundation
The goal of this project is first to learn an adversarial agent informed by explainable AI. Using this learned agent, we then unveil the weakness of target agents and adversarially train them accordingly. Through a robustness check, we evaluate the enhanced agents. If a strengthened agent fails the adversary-resistance check, we fall back on formal verification and program synthesis techniques. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Dongwon Lee
Sponsoring Agency: National Science Foundation
This collaborative project between Penn State and Tuskegee University proposes to improve the solutions for the intrusion detection tasks by means of the Anomaly Detection framework in cyber systems by incorporating recent advancements in big data and machine learning techniques. In this project, we explore how to advance existing Anomaly Detection Systems (ADSs) to prevent more diverse and challenging types of network intrusions with higher detection accuracies. Recent advances in big data and machine learning, especially deep learning, provide an unprecedented opportunity for building highly effective ADSs. Therefore, the team will investigate methods in various data science and machine learning fields, and seek to exploit them in the context of network intrusion detection. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Ting Wang
Sponsoring Agency: National Science Foundation
We propose a new disciplinary concept of computational Screening and Surveillance (CSS) that utilizes edge learning to collect, analyze and interpret both physical and physiologic data of human subjects, to detect early indicators of diseases, and monitor health changes in both individuals and populations. Real-time information, knowledge, and insights from extreme-scale CSS will revolutionize our understanding, prediction, intervention, treatment, and management of acute/infectious (e.g. flu, COVID), chronic physical and psychological/psychiatric diseases, resulting in huge savings for numerous diseases each costing hundreds of billion dollars every year. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: National Science Foundation
In this proposal, we ask one critical question --- assuming we will never have representative labels, what can we do to significantly improve the adaptability and resilience of learning-based defenses with extremely limited labeling capacity? While the problem looks challenging, recent progress in self-supervised learning has shown great promise to perform complex learning tasks with limited labels. Self-supervision is about designing pretext learning tasks to better utilize unlabeled data and obtaining supervision from the data itself. While most existing efforts focus on computer vision and natural language process tasks, we believe some of the fundamental ideas can significantly benefit the security community to address the concept drift problem. In this proposal, we want to combine the idea of self-supervision with the domain-specific insights in malware detection to build new solutions to combat concept drift. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Nicklaus Giacobe, Mike Hills
Sponsoring Agency: National Security Agency
The CYSP program provides scholarship opportunities to College of IST students interested in pursuing employment with the Department of Defense. It also provides faculty with the opportunity to compete for capacity building project funds to develop educational products of general use to the wider Center for Academic Excellence community. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Dongwon Lee
Sponsoring Agency: National Science Foundation
In this project, we propose a flexible framework, named as SAGA, where scholars can easily create cybersecurity case studies (similar to business case studies) that have AI components. Further, by adopting the notion of “citation” in academic world and implementing it using public platforms (e.g., arXiv, Github, Kaggle), SAGA enables the developed case studies to be easily found and shared in the community, and the authors of case studies to be rightfully attributed for their efforts, thereby encouraging more participation from scholars in creating and sharing case studies. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Suhang Wang, Dongwon Lee
Sponsoring Agency: National Science Foundation
This project explores effective labeled data generation via generative adversarial learning and proposes novel approaches based on generative adversarial learning for effective labeled data generation to facilitate deep learning with limited label information, investigates associated fundamental research issues and develops effective algorithms. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher: Peng Liu
Sponsoring Agency: National Science Foundation
This research project seeks to develop new techniques and tools for insecurity analysis of middleware on mobile platforms (MoMP) like Android Framework and consequently lead to more secure and trustworthy computing environments for the huge number of smartphone and Internet-of-Things (IoT) device users. The project will develop new architectural designs, algorithms and techniques for precise and automated insecurity analysis of MoMP. Learn more...
Research Areas: Privacy and Security, Social and Organizational Informatics
Term: -
Researcher: Anna Squicciarini
Sponsoring Agency: National Science Foundation
This project investigates the complex chaotic behaviors that can emerge as a result of evolutionary dynamics on networks, develops techniques for dynamic control, and studies the problems associated with privacy and fairness among agents in these systems. The work focuses on semi-autonomous agents that interact with each other in a network and alter their instantaneous mixed strategies through evolutionary dynamics, with an emphasis on flocking and consensus dynamics. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Linhai Song
Sponsoring Agency: National Science Foundation
This project aims to facilitate informed vaccination decisions. The research aims to illuminate how people’s vaccination decisions evolve in response to their social context. The project’s novelty is to follow participants over time to evaluate their real-world decision-making about vaccination. The project will advance the state-of-the-art on risk communication during crises and decision-making under uncertainty. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Biomedical and Health Informatics, Privacy and Security
Term: -
Researcher: Anna Squicciarini, Peter Forster
Sponsoring Agency: National Science Foundation
This project aims to build mathematical and data-driven models to understand the dynamics of extremist groups at scale, the patterns of their influence, and integrated micro (individual-level) and macro (group-level or system-level) data-driven models that can guide future interventions. This project provides a greater understanding of users' behavioral patterns and social dynamics related to online extremism. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher: Ting Wang
Sponsoring Agency: International Business Machine Corporation
The goal of Hardening and Orchestrating Responses Under Stress (HORUS) project is to enable dynamic response and adaptive protection for cyber hunting scenarios, leveraging (1) a cognitive threat analysis process to choose from effective protective responses and actions, (2) a game-theoretic model for action selection and attack anticipation, and (3) adversarial analysis of threats and hardening of detectors. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Dongwon Lee, Anna Squicciarini, Peter Forster
Sponsoring Agency: National Science Foundation
This project will expand the capability and involvement of Penn State students State in cyber-relevant disciplines. To support student needs, we have implemented a flexible and strong Scholarship for Service (SFS) program, based on customized mentoring for each student. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Dongwon Lee
Sponsoring Agency: National Science Foundation
This is a project to study what works to help students learn more effectively in the context of the ASSISTments system, an online system that provides both assistance to students and real time assessment data to teachers. This project will increase the assistance provided by the teacher and machine learning by incorporating video suggestions, such as those produced by the Kahn academy, targeted to the needs of the student. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Anna Squicciarini
Sponsoring Agency: National Science Foundation
This research project seeks to address privacy issues by developing a new location privacy framework for workers in vehicle-based spatial crowdsourcing. The project will start with developing new adversarial models to capture the network-constrained mobility features of multiple vehicles operating over roads. As a countermeasure of the adversarial models, the project will develop a new location obfuscation paradigm that can effectively protect vehicles' location privacy even assuming that adversaries use vehicles' mobility features for inference attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Shomir Wilson, C. Lee Giles
Sponsoring Agency: National Science Foundation
We propose to build a large-scale, longitudinal, annotated, and searchable resource of privacy policies, terms of service agreements, cookie policies, and other related documents for the privacy research community. This resource, which we name PrivaSeer, will serve three simultaneous roles: (1) a search engine for privacy documents (i.e., privacy policies plus other species of relevant text); (2) a source of corpora for use by the research community; and (3) an API for privacy-enhancing technologies to draw privacy information from on demand. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Dongwon Lee, Aiping Xiong
Sponsoring Agency: National Science Foundation
This funding establishes a new Research Experiences for Undergraduates (REU) Site at Pennsylvania State University. An interdisciplinary team of experienced faculty mentors will guide undergraduate students in summer research projects focused on applying machine learning methods to solve cybersecurity problems, particularly cyber-attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: National Science Foundation
This project will provide much-needed automation for reproducing kernel bugs and vulnerabilities. If successful, the project will significantly advance computer security (for kernel vulnerability analysis) and contribute to the field of software engineering (for bug diagnosis and assessment). By improving the reproduction rate of kernel bugs, this project will also help with other parallel efforts for vulnerability patching and remediation. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: Office of Naval Research
This research aims to explore, design and develop a series of technical approaches to ease the development of working exploits and escalate the exploitability for vulnerabilities. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Ting Wang
Sponsoring Agency: National Science Foundation
This project aims at understanding the security threats incurred by reusing third-party models as building blocks of machine learning (ML) systems and developing tools to help developers mitigate such threats throughout the lifecycle of ML systems. Outcomes from the project will improve ML security in applications from self-driving cars to authentication in the short term while promoting more principled practices of building and operating ML systems in the long run. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Linhai Song
Sponsoring Agency: National Science Foundation
This project aims to better understand Rust’s memory bugs and build novel static/dynamic tools to combat Rust’s memory bugs. This project contains three components: (1) a comprehensive taxonomy of Rust’s memory bugs, (2) novel static techniques to identify memory bugs in interior unsafe functions, and (3) novel fuzzing techniques enhanced by the safe/unsafe information in Rust. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Ting Wang
Sponsoring Agency: National Science Foundation
The transformative nature of this project is to completely rethink how to define and implement the interpretation of DNNs and how to exploit this interpretability as a bridge to understand and control the DNN behaviors. This project aims to develop RIDDLE, a new interpretable deep learning framework that is reliable, interactive, and debuggable. Learn more...
Research Areas: Privacy and Security
Term: -