Dallas Snider

An introductory presentation on the Internet of Things at the 2013 ITEN Wired Conference in Pensacola Beach, Florida.

A question and answer session covering various topic with Microsoft's SQL Server Analysis Services

Presentation given as part of the 2012-2013 UWF Biology Department Seminar series

MSSQLTips.com webinar on troubleshooting and resolving issues with network bottlenecks affecting SQL Server.

Presentation of our research project and results at the Security and Software Engineering Research Center November 2013 Showcase.

Presentation at the 2014 ITEN Wired Summit in Pensacola Beach, FL. Results from a research project at the University of West Florida and sponsored by Northrop Grumman Aerospace Systems show the effectiveness of using concept maps to help exchange knowledge of security requirements throughout an organization.

The 11th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT 2014)

2014 Northwest Florida IT Pro Camp

MSSQLTips.com webinar to introduce neophytes to SQL Server Analysis Services, which is the analytical component of Microsoft’s SQL Server Business Intelligence Solution.

Presented at IEEE SoutheastCon 2015. With the increase in smart, LEED-certified buildings there comes an increase in the amount of time-series data generated by the sensor networks within these buildings. Extracting useful information from the sensor network data can pose a challenge. While diurnal and seasonal patterns of electrical demand are well known from traditional metering systems, smart building sensor networks can provide insight into abnormalities or previously unknown patterns in electrical demand. In this paper, we demonstrate how to mine the data for these unknowns through the analysis of the frequency components of the time-series electrical demand data. The data for this study was collected from an LEED-certified building over twelve consecutive months with separate data feeds for the electrical demand from the heating, A/C, ventilation, lighting and miscellaneous systems. We employed Fourier methods to transform the data from the time domain to the frequency domain and then used similarity measures to look for similarities and outliers among the differing systems.

Presentation at IEEE SoutheastCon 2015. Today’s warfighter is increasingly dependent on networked systems and information from unmanned aerial vehicles to provide up-to-the-minute conditions on the battlefield; therefore the network must continually perform at optimum levels. One of the problems encountered in complex air-to-ground networks is that all possible hardware, software and network configurations that will be encountered in the field cannot be pretested; therefore there is a need to provide a method for studying the interaction among diverse hardware and software components and identifying potential network bottlenecks in air-to-ground networks and their causes before they become critical. In this paper, we demonstrate our accomplishments in building a robust and scalable simulation of an air-to-ground network environment based on wired and wireless network emulation using widely available software tools. We will also present our design for data capture and evaluation that support hypothetical, what-if scenarios for testing network performance bottlenecks.

Presented at IEEE SoutheastCon 2016. Software defined networks and network function virtualization are providing much needed agility to network and system administrators attempting to meet ever changing demands from their stakeholders. However, there exists a need for tools to assist in the configuration, deployment, testing and knowledge transfer of these networks and their components. In this paper, we present our plans to develop a set of open source tools to assist with the aforementioned tasks. The goal is to apply existing disparate pieces of technology so they may work together to assist with the management of virtual network environments.

Presented at IEEE SoutheastCon 2016. In this paper, we describe a process that has been developed to transfer network intrusion data captured by Fail2ban to an adaptive enterprise intrusion detection and prevention system. The process involves software agents that we have created that are interconnected to a central behavior analysis database service where each software agent records attack meta-information collected during previous intrusion attempts. These distributed agents are the first phase of an overall plan to create a smarter network defense system through the collection and analysis of network signatures generated by real security threats. The central database to which the agents report warehouses and analyzes the meta-information collected by the interconnected agents. The agents can then utilize both instantaneous and historical data by integrating rules derived from the data collection and analysis process into intrusion prevention policies. The final result will be a modular and scalable network defense system that should be more responsive and adaptable to imminent threats.

Presented at the International Conference on Artificial Intelligence in July 2015. Described our plans to create a smarter network defense system through the collection and analysis of network signatures generated by real security threats. To meet this goal, we plan to create software agents interconnected to a central behavior analysis database service where each software agent records attack meta-information collected during previous intrusion attempts. The central database warehouses and analyzes the meta-information collected by the interconnected agents. The agents can then utilize both instantaneous and historical data by integrating rules derived from the data collection and analysis process into intrusion prevention policies. The result is a modular and scalable network defense system that should be more responsive and adaptable to imminent threats.