Unmanned Aerial Vehicle Attack Detection using Snort

Abstract

: In recent times, security issues relating to unmanned aerial vehicles (UAVs) and drones have anticipated a staid attention from research communities in various domains in the form of networking, communication, and civilian as well as in defence zone. It has its widespread functionality in the area of agriculture, commerce, and transportation, the use of unmanned aerial vehicles (UAVs)/ drones, is increasing. The ground control systems (GCS) are used to remotely monitor UAVs over the network. Since UAVs are vulnerable to security risk, they become the targets of various attacks such as GPS spoofing, jamming attack, network attacks and many other forms so to tackle with such issues the prime concern will be to identify these attacks followed by to prevent the UAVs or drones from UAV attacks. On contrary network-controlled UAVs however are equally vulnerable to threats like DOS attacks, GPS spoofing etc. In this work a network surveillance approach is projected for UAV attack detection system by means of Snort. Snort uses a set of guidelines and rules set by the user itself to help in identifying the malicious network behaviour and to locate packets that fit them and create user warnings with those rules. It is an open-source tool that records traffic analysis and packets in real time.