Recent Research Grants
CSR: Small: Collaborative Research:
Tuning Extreme-scale Storage Stack through Deep Reinforcement Learning
PI: Yong Chen
Grant Period: October 2018 – September 202
Project Overview: This research explores the feasibility of leveraging deep reinforcement learning to optimize HPC storage systems by: (a) Creating a deep learning based HPC storage stack model; (b) Remodeling existing HPC storage stack to support automated configuration and tuning; (c) Collecting training datasets and training the storage stack model; and (d) utilizing the model as a responsive and playable virtual environment to learn the best policy to tune parameters.
Improving Student Learning and Engagement in Digital Forensics through Collaborative Investigation of Cyber Security Incidents and Simulated Capture-the-Flag Exercises
PI: Akbar Namin
Grant Period: September 2018 – August 2020
Project Overview: This project will enhance cybersecurity education by: 1) Creating an online repository
of cybersecurity learning resources and open-source tools with a focus on digital
forensics and security incidents. 2) Developing a number of instructional modules
along with hands-on experiences and training videos for open-source digital forensics
tools. 3) Implementing a collaborative educational capture-the-flag tool with the
capability of continuous creation and integration of cyber incidents scenarios. 4)
Including a set of cyber-attack scenarios based on recent real-world incidents that
leverage the use of puzzles in the capture-the-flag tool.
SHF: Small: Collaborative Research:
Uncovering Vulnerabilities in Parallel File Systems for Reliable High Performance Computing
PI: Yong Chen
Grant Period: August 2017 – July 2020
Project Overview: This research aims to design innovative methodologies that scrutinize parallel file systems, the major storage software which empowers HPC platforms, and uncover the issues in parallel file systems that can lead to data loss under various failure scenarios. Such an effort is a fundamental step towards building highly reliable HPC systems and meet the demand of data-driven scientific discovery. In addition, this project integrates the research activities with education and outreach efforts to train broadly inclusive and globally competitive science workforce.
Partitioning Large Graphs in Deep Storage Architecture
PI: Dai Dong
Grant Period: April 2018 – October 2018
Project Overview: This project includes two synergistic research tasks, together forming a novel graph partitioning solution for deep storage architecture. The first task focuses on an online graph placement algorithm, which could instantly distribute the continuously arriving graph vertices and edges to proper server and specific internal storage layer based on an elaborate heuristic score. Building upon the first task, the second task focuses on adjusting current partitions dynamically according to the workloads.
Enhancing Water Resource Management and Infrastructure Improvement through Sensing, Computation, and Community Engagement
PI: Jin Fang
Grant Period: August 2017 – August 2019
Project Overview: This planning project will hold (1) a focus group-based study to discuss the persisting, urgent problems among water stakeholders, and (2) a workshop where stakeholders will have the opportunity to learn about challenges from a range of perspective, share advances in water technology and infrastructure, and discover potential research opportunities to address the problems exposed. Thus, this planning project will engage a wide range of water-related stakeholders and promote effective water conservation strategies to protect the region's water supply.
Applied Data Science for Cyber Security
PI: Abdul Serwadda
Grant Period: Feb 2018 – Jan 2021
Project Overview: This award creates a new Research Experiences for Teachers (RET) Site focused on data science and cybersecurity at the Texas Tech University. Each summer, ten high school science and mathematics teachers will participate in summer research activities with faculty in labs at Texas Tech University. The teachers will be recruited from the area served by the Texas Educational Service Center Region 17, which includes the city of Lubbock and the surrounding rural areas. Cyber security is important for many technical applications as well as for the daily lives of most citizens. Teachers in this site will apply fundamental data science techniques and learn basic cyber security principles while investigating real world problems involving cybersecurity. The participating teachers will translate their research experiences and knowledge into classroom practice by developing instructional modules and course materials that they will introduce in their classrooms and share with other teachers in their school districts.
User-Centric Design of a Sonification System for Automatically Alarming Security Threats and Impact
PI: Akbar Namin
Grant Period: September 2016 – August 2020
Project Overview: This project translates security warnings and threats into various forms of sounds. The introduced user-centric sonification takes a security warning and maps out the risks and consequences associated with the underlying security threat into a certain type of sound that reflects the emotional feelings of the potential risks and harm caused by the threat. The project builds a repository of sounds tagged with their emotional impacts such as fear, happiness, and sadness. The project also builds a second repository of security threats tagged with similar emotional impacts such as fear of loss of sensitive information, impersonation, and privacy exposure.
Spoof-Resistant Smartphone Authentication using Cooperating Wearables
Co-PI: Abdul Serwadda
Amount: $315,999.00 (TTU Share: $72,000)
Grant Period: September 2015 – August 2019
Project Overview: This research is developing methods that leverage a multitude of sensors embedded in hand-held and wearable devices (e.g., smart watches, smart glasses and brain-computer interfaces) for strong user authentication to smart phones. The current point-of-entry solutions, largely based on weak static credentials, such as passwords or PINs for authentication to smart phones are not sufficient because once such credentials are compromised (which is very likely given the many vulnerabilities of passwords), the attacker may gain unfettered access to the smart phone. In this light, it becomes necessary to constantly protect the smart phone even after the attacker has already bypassed the point-of-entry authentication functionality. This research aims to address this problem by developing methods through which cues extracted from one or more wearable devices will be used in conjunction with cues extracted from the phone itself to continuously and unobtrusively verify the authenticity of the user.