Our faculty work together with graduate and undergraduate students, exploring a variety of cybersecurity research projects including information and network security, IoT security, secure software engineering, geographic visualization, and applications of artificial intelligence and machine learning for cybersecurity. Take a moment to browse through our current projects and faculty research profiles for more information on their areas of research.
Pioneering the Science of Security
The U.S. Government suffered over 77,000 cybersecurity incidents in 2015 alone. We’re dedicated to lowering that number every day, synergistically initiating and executing groundbreaking research that solves real world issues – a step ahead of the adversary.
Building Automation Systems and Cyber Security: A Multiple Discipline Perspective
In this conference paper, UWF researcher Dallas Snider, Ph.D. collaborated with Glenda Mayo, Ph.D. from the University of North Carolina – Charlotte to create an IRB-approved survey in which participants were asked to provide their awareness of cyberattacks and vulnerabilities from the building automation systems perspective. The survey was targeted to professionals in information technology and building automation systems with the goal of determining whether or not a knowledge gap exists.
Faculty involved: Glenda Mayo and Dallas Snider
Security for DevOps Deployment Processes
DevOps is an emerging collection of software management practices intended to shorten time to market for new software features and to reduce the risk of costly deployment errors. In this project we examine the security implications of two of the key DevOps practices: automation of the deployment pipeline using a deployment toolchain and "infrastructure-as-code" to specify the environment of the deployed software.
Faculty involved: Norman Wilde and Brian Eddy
DNS RPZ Evaluation (ongoing project)
Response Policy Zones (RPZs) are an extension to Domain Name Services (DNS) that offer a strong defense against many common cyberattack techniques. However RPZs are currently not widely used. We are conducting an evaluation of RPZ in collaboration with the Global Cyber Alliance's Internet Immunity initiative.
Faculty involved: Norman Wilde
Enhancing Computer and Network Security using Machine Learning
This project explores the development and evaluation of machine learning and deep learning tools and methods for enhancing computer and network security including malware analysis and network traffic analysis.
Faculty involved: Eman El-Sheikh
Combining the Extended Risk Analysis Model and the Attack Response Model to Introduce Risk Analysis
This paper uses the Extended Risk Analysis Model to introduce risk analysis in a classroom setting. The four responses to an attack, avoidance, transference, mitigation, and acceptance are over laid on the model to aid in the visualization of their relationship. It then expands and updates the cyber insurance portion of the Extended Risk Analysis Model.
Faculty involved: Randy Reid
Influences on Ransomware's Evolution and Predictions for the Future Challenges
In this paper, we analyze the evolution of ransomware from the perspective of what makes an individual or an organization susceptible to the succumbing demands of ransomware. Finally, we conclude with few possible predictions of future trends of ransomware.
Faculty involved: Ezhil Kalaimannan and Anthony Pinto
End User Error in CyberSecurity Breaches
This research involves several aspects of end user error in cyber security including development of a vocabulary (and later ontology) pertaining to end user error, and fine grained characterizations of end user error based upon analysis of textual descriptions in a database of 5,000 data breach incidents. I am also writing a case study on the implementation of UWF's security program.
Faculty involved: John Coffey and Bernd Owsnicki
Crime Scene Investigation [CSI] in Digital Forensics
Digital forensic investigation refers to the use of science and technology in the process of investigating a crime scene so as to maximize the effectiveness of proving the perpetrator has committed crime in a court of law. In this research, we propose to develop efficient computational models and heuristic algorithms which can improve the overall effectiveness of a crime scene investigation procedure in Digital Forensics.
Faculty involved: Ezhil Kalaimannan
Passive Intrusion Detection System [IDS] Alarm Analysis
Securing and defending computing networks has become a matter of growing importance attracting the attention of both practitioners and researchers. One of the significant challenges presented by IDSs, is how do network managers prioritize and commit resources to investigate notification by an IDS of potential threats to the network. By developing mathematical and algorithmic models for this problem, a novel method is presented to illustrate how network managers can optimally allocate their limited resources for investigating IDS notifications.
Faculty involved: Ezhil Kalaimannan
Faculty Profiles at a Glance
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