Keywords: Mobile botnet, symmetric mobility model, asymmetric mobility model, Long Term Evolution networks, distributed denial of service, Riverbed modeler simulator, segment-based trajectory, real traces dataset.
This paper studies the impact of a mobile botnet on a Long Term Evolution (LTE) network by implementing
a mobile botnet architecture that initiates a Distributed Denial of Service (DDoS) attack.
To understand the behavior of the mobile botnet, a correlation between the mobile devices’ mobility
and the DDoS attack is established. Real traces of taxi cabs are used to simulate the mobile devices’
trajectory movements. Indeed, the impact of the random patterns of movements’ behavior (so-called
Asymmetric Mobility Model (AMM)) (resp. the uniform patterns of movements’ behavior (so-called
Symmetric Mobility Model (SMM)) on the mobile botnet’s behavior are studied under a DDoS attack
scenario. This reveals the advantage of deploying the SMM model compared to the AMM model,
with respect to the number of infected mobile devices, task processing time, traffic load and response
time of the victim server, and CPU resource consumption.