Title
Enhancing SCADA System Security Via Spiking Neural Network
Document Type
Student Presentation
Presentation Date
4-24-2020
Faculty Sponsor
Dr. Kurtis Cantley
Abstract
Supervisory Control and Data Acquisition (SCADA) systems are industrial control systems used to monitor and maintain in telecommunications, transportation, energy, etc. These systems are becoming increasingly networked for ease of access and sharing resources. This increases the security risks for these systems and the demand for identifying threats. One common industrial control protocol is Distributed Network Protocol (DNP3) which is utilized in water and electric utilities. In addition, spiking neural networks have the capabilities of interpreting spatio-temporal data. We will be implementing a spiking neural network to detect network threats using DNP3 network data as input data. DNP3 data will examined under both typical network conditions and network conditions similar to a network attack. The neural network’s ability to differentiate these conditions will be evaluated by an error calculation.
Recommended Citation
Kramer, Kyle D.; Ivans, Robert; Fisher, Nathaniel; and Cantley, Kurtis, "Enhancing SCADA System Security Via Spiking Neural Network" (2020). 2020 Undergraduate Research Showcase. 98.
https://scholarworks.boisestate.edu/under_showcase_2020/98