Device classification for telecommunication industry

As information technology (IT) and telecommunication systems continue to grow in size and complexity, the types and the number of telecommunication and networking devices required to support provider’s services have surged. Identifying these devices in a network is a challenging task, since they usually lack proper or accurate documentation describing their operating characteristics. Accurate device identification allows an enterprise to optimize the usage of resources efficiently and to respond quickly to any changes in the observed characteristics especially in cases of device failures or security breaches. This project will focus on the development of machine learning based algorithms for identification or classification of network devices based on their operating characteristics, functionality, connectivity and exposure to different environments for highly complex, poorly characterized and rapidly changing IT systems. Further, determining how inconsistencies or gaps in data will affect the accuracy of a classifier in identifying these devices is also an inherent part of this project.

Faculty Supervisor:

Kin Li

Student:

Partner:

TELUS (Vancouver, BC)

Discipline:

Engineering

Sector:

Information and cultural industries

University:

University of Victoria

Program: