Detection of Cyber-attacks and Node Recovery in Mobile Networks
Abstract
According to the current statistics, 83.96% of the world’s population owns a
smartphone. The number raises daily due to the increasing need for internetpowered services. For various reasons including monetary reasons, and
market competition, malicious actors attack both end user devices and
network infrastructures to disrupt communication channels.
In this project, we are focusing on an infrastructure targeted Denial of Service
attack known as Signaling amplification attack. The network attach procedure
involves a large collection of data between user equipment, radio access network
and mobility management entity which Cybercriminals have abused to initiate
this same process with the intention of overwhelming the network
infrastructure hence denying service to the legit network users.
This report therefore presents the development and simulation of a deployed
machine learning model that will enable timely detection of a signaling
amplification attack, isolation of the malicious source from the network and
recovery mechanism when the particular node’s behavior normalizes.