Drone classification and identification system by phenome analysis using data mining techniques

Abstract

The Unmanned Aerial Vehicle (UAV) technology has gained immense popularity in recent times. The technology has witnessed enhanced capabilities in terms of payload, greater distance operability and stability in hovering. The growing sophistication in the UAV platform can be compared to the stability and efficiency delivered by manned aircraft. The UAV platform complimented by its size, absence of thermal signature and battery operated motors offer it incredible amount of stealthy performance capabilities. Recent times have been a witness to the prowess of UAV technology being exploited for illegal activities such as criminal, terrorist purposes, and drug delivery platform across the border. The drone technology has also been misused as a platform by, nefarious individuals who use the UAV technology with a unique eves dropping payload to launch network intrusion into corporations by hovering their UAV over the corporate infrastructure. According to Federal Aviation Administration (FAA), commercial airline pilots have reported over a 100 close call situations or drones in visible proximity of the aircrafts at the airports. Sales of Drone have been estimated to cross the 700,000 mark in 2015, an increase of 63% over its sales in 2014. This paper outlines the challenges in drone detection, its identification and classification to preempt any possible malicious intent.