Title

Monitoring and Detection of Network Anomalies

Presenter Information

Richard Snyder

Department

Electrical and Computer Engineering

Major

Computer Engineering

Research Advisor

Çetinkaya, Egemen K.

Advisor's Department

Electrical and Computer Engineering

Abstract

Networks face a variety of attacks from both interior and exterior sources. Timely detection and reaction to these attacks is important for the operation and management of networks. This research will focus specifically on detecting network anomalies on the Missouri S&T network. Network traffic data will be gathered from the Missouri S&T network using honeypots. This data will then be analyzed to discern anomaly detection criteria. It will be possible to write an anomaly detection program using the discerned criteria.

Biography

Eli Snyder is a junior standing student majoring in computer engineering. This past summer, Eli was an intern at Los Alamos National Laboratories. During this internship Eli learned how to set up a Linux supercomputer and attended talks about new and upcoming technology. During the teaching, security was emphasized, as Los Alamos protects national secrets. These security topics interested Eli. To further this interest, Eli participated in the OURE program fall term of 2016 and the spring term of 2017, researching methods to improve network security through application of graph theory. He enjoyed the challenge presented in optimizing the logical and physical networks of the largest internet service providers. He plans to continue researching a different facet in network security until he graduates.

Presentation Type

OURE Fellows Proposal Oral Applicant

Document Type

Presentation

Location

Turner Room

Start Date

4-11-2017 2:40 PM

End Date

4-11-2017 3:00 PM

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Apr 11th, 2:40 PM Apr 11th, 3:00 PM

Monitoring and Detection of Network Anomalies

Turner Room

Networks face a variety of attacks from both interior and exterior sources. Timely detection and reaction to these attacks is important for the operation and management of networks. This research will focus specifically on detecting network anomalies on the Missouri S&T network. Network traffic data will be gathered from the Missouri S&T network using honeypots. This data will then be analyzed to discern anomaly detection criteria. It will be possible to write an anomaly detection program using the discerned criteria.