Automatic Modulation Classification using Deep Learning for wireless security applications

Applications of wireless security approaches are increasing in number by the day. One such application is detection and interception of rogue aerial intruders. Drone technology is growing at a tremendous pace and is expected to be a $12 billion industry by 2021. Coupled with this growth comes the increasing threat of rogue intruders disrupting day-to-day activities and sensitive infrastructure. Towards this end, rogue drone detection has become an important industry by itself and focuses on securing the airspace around us. In this project, we aim to build a deep learning framework augmented with traditional signal processing techniques to model and classify unknown wireless signals. This model would be able to learn from the time domain information of the signal (amplitude, phase) and be robust in conditions with varying Signal-to-Noise Ratio (SNR). TO BE CONT’D

Faculty Supervisor:

Vijay Bhargava

Student:

Kevin Dsouza

Partner:

Skycope Technologies Inc

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program: