An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the
  Emerging Zero Touch Cellular Networks
Aneeqa Ijaz Waseem Raza Hasan Farooq Marvin Manalastas Ali Imran
Deep automation provided by self-organizing network (SON) features and their
emerging variants such as zero touch automation solutions is a key enabler for
increasingly dense wireless networks and pervasive Internet of Things (IoT). To
realize their objectives, most automation functionalities rely on the
Minimization of Drive Test (MDT) reports. The MDT reports are used to generate
inferences about network state and performance, thus dynamically change network
parameters accordingly. However, the collection of MDT reports from commodity
user devices, particularly low cost IoT devices, make them a vulnerable entry
point to launch an adversarial attack on emerging deeply automated wireless
networks. This adds a new dimension to the security threats in the IoT and
cellular networks. Existing literature on IoT, SON, or zero touch automation
does not address this important problem. In this paper, we investigate an
impactful, first of its kind adversarial attack that can be launched by
exploiting the malicious MDT reports from the compromised user equipment (UE).
We highlight the detrimental repercussions of this attack on the performance of
common network automation functions. We also propose a novel Malicious MDT
Reports Identification framework (MRIF) as a countermeasure to detect and
eliminate the malicious MDT reports using Machine Learning and verify it
through a use-case. Thus, the defense mechanism can provide the resilience and
robustness for zero touch automation SON engines against the adversarial MDT
attacks