Research Projects

Students Working on Research Project

 

▼   Moving Target Defense (MTD)

Moving Target Defense (MTD) is an approach to security to provide a continually changing target, thus increasing the cost to the traditional methods to attacking a static system. We are investigating applying MTD approaches through the use of hardware/software partitioning using reconfigurable Field Programmable Gate Arrays. Additionally we are examining potential uses to use MTD's to protect critical infrastructure and to determine their possibility to protect against side-channel emanations.

USA SoC Students
Current:
Cordell Davidson, John Dombrowski, Tristen Higgenbotham (College of Engineering)

Graduated:
Lindsey Whitehurst

USA Collaborating Partners:
School of Computing: J. Todd McDonald, Mark Yampolskiy, William B. Glisson
School of Engineering: Samuel Russ, Waleed Al-Assadi, Tom Thomas

▼   Securing Protocols used in Transportation Systems

Modern Transportation systems, such as automobiles and commercial aircraft, are becoming more reliant on computing based systems for controlling their operation. This aspect combined with the ever increasing connectivity to external environments, such as cell phones, laptops, the Internet, and other vehicles, provides the potential of a cyber attack to interfere with normal operations. We are investigating new protocols and developing new techniques to formally verify and model security aspects as these systems transition to new communication architectures

USA SoC Students
Current:
Adam Brown, Tyler Trigg

Graduated:
Paul Carsten

USA Collaborating Partners:
School of Computing:  J. Todd McDonald, Mark Yampolskiy, William B. Glisson
School of Engineering: Samuel Russ, Waleed Al-Assadi

Outside Collaborating Partners:
Boeing Corporation

▼   Additive Manufacturing (3D Printing) Security

The rapid advancement of additive manufacturing (AM, a.k.a 3D Printing) raises concerns about its security. So far, two major security threat categories have been identified in research literature: (i) violation of Intellectual Property (IP) and (ii) sabotage (eventually, weaponization) of AM process. The ongoing projects aim to overcome challenges and develop solutions addressing both of these threats. AM security is a cutting-edge, highly interdisciplinary research field. Various aspects require knowledge in disciplines like cyber-security, CPS security, material science, and mechanical engineering.

USA SoC Students:
Jacob Gatlin, Samuel Moore, Andrew Slaughter, Adam Minor

Outside Students:
Sofia Belikovetsky (Ben-Gurion University)

USA Collaborating Partners:
School of Computing: Alec Yasinsac
School of Engineering: Samuel Russ, Kuang-Ting Hsiao
College of Education and Professional Studies: Brenda Litchfield

Outside Collaborating Partners:
L. Jane Davis (Springhill Medical Center, Mobile, AL)
Yuval Elovici (Ben-Gurion University)
Wayne E. King (Lawrence Livermore National Laboratory)
Michael Kretzschmar (NATO BICES Group Executive)
Manyalibo Matthews (Lawrence Livermore National Laboratory)
Gregory Pope (Lawrence Livermore National Laboratory)
Anthony Skjellum (Auburn University)

▼   Semantic Technologies and Biomedical Knowledge Engineering

The OmniSearch project is supported by an active NIH/NCI grant (U01CA180982). It aims to develo a semantic search tool to assist cancer biologists in unraveling critical roles of microRNAs (miRs) in human cancers in an automated and highly efficient manner. The project will handle the significant challenge of data sharing, date integration, and effective search in miR research in oncology. A Web-based, user friendly interface is publicly available at: http://omnisearch.soc.southalabama.edu/ui.


USA SoC Students:
Vikash Jha, Mohan Kasukurthi, Harrison J. Strachan


Outside Students:
Nisansa de Silva (University of Oregon)

USA Collaborating Partners:
Biology: Glen M Borchert
School of Computing: Jingshan Huang
Mitchell Cancer Institute: Zixing Liu, Ming Tan

Outside Collaborating Partners:
Judith A. Blake (Jackson Laboratory)
Dejing Dou (University of Oregon)
Karen Eilbeck (University of Utah School of Medicine)
Darren Natale (Georgetown University Medical Center)
Alan Ruttenberg (University of Buffalo - SUNY)

▼   Computer Science Education

Investigating the benefits of introducing specific strategies for team building with the aim to build team cohesiveness in team-based learning courses.

USA Collaborating Partners:
School of Computing: Dawn McKinney
College of Education and Professional Studies: Brenda Litchfield

Outside Collaborating Partners:
L. Jane Davis (Springhill Medical Center, Mobile, AL)

▼   Targeted Pattern Mining

Itemset Tree is a data structure that, along with associated search algorithms, permits the ability to conduct targeted association mining. Association mining is a type of data mining that seeks to find correlations between multiple variables within a database. Current research efforts include improving the efficiency of targeted association mining and modifying the Itemset tree and algorithms to support advanced association and pattern mining.


USA SoC Students:
Vishal Bohara, Jay Lewis

USA Collaborating Partners:
School of Computing: David Bourrie, Tom Johnsten

Outside Collaborating Partners:
Alaaeldin M. Hafez (King Saud University)
Jennifer Lavernge (UL Lafayette)
Vijay Raghavan (UL Lafayette)

▼   Social Media Mining

Social media has become a much discussed source of information; however, much of the analysis tends to be along the line of trending topics and terms. There is a growing emphasis on extracting addition types of information from the media; we have been pursuing two different efforts. One deals with detecting emerging events such as bomb threats, fires, road accidents, and drug recalls; the goal is to detect these events (and track them) within one to three minutes of their initial mention. The second centers around detecting new adverse drug reactions by analyzing Twitter data. This requires temporal reasoning, graph analysis and the ability to filter out spurious drug and reaction relationships.

Students:
Harika Karnati (UL Lafayette)
Satya Katragadda (UL Lafayette)
Murali Pusala (UL Lafayette)

USA Collaborating Partners:
School of Computing: Ryan Benton

 

Collaborating Partners:
Chaomei Chen (Drexel University)
Weimao Ke (Drexel University)
Vijay Raghavan (UL Lafayette)
Xiaohua Tony Hu (Drexel University)

▼   Action Rule Mining

Action rules are constructs that provide guidance on what actions (i.e. changes to attribute values) should be made to convert a set of objects from an undesirable state to a more desirable state. For example, assume that you are seeking to determine what can be done to reduce the severity of traffic accidents. A potential action rule would state, if you add streetlights to a street with none, a significant number of accidents that result in severe injury would be reduced to accidents classified as minor. Current research includes the development of more efficient and effective algorithms for discovering action rules.


USA SoC Students:
Vishal Bohara, Shawyn Kane

USA Collaborating Partners:
School of Computing: Ryan Benton, Tom Johnsten