  The holy grail of networking is to create \textit{cognitive networks} that
organize, manage, and drive themselves. Such a vision now seems attainable
thanks in large part to the progress in the field of machine learning (ML),
which has now already disrupted a number of industries and revolutionized
practically all fields of research. But are the ML models foolproof and robust
to security attacks to be in charge of managing the network? Unfortunately,
many modern ML models are easily misled by simple and easily-crafted
adversarial perturbations, which does not bode well for the future of ML-based
cognitive networks unless ML vulnerabilities for the cognitive networking
environment are identified, addressed, and fixed. The purpose of this article
is to highlight the problem of insecure ML and to sensitize the readers to the
danger of adversarial ML by showing how an easily-crafted adversarial ML
example can compromise the operations of the cognitive self-driving network. In
this paper, we demonstrate adversarial attacks on two simple yet representative
cognitive networking applications (namely, intrusion detection and network
traffic classification). We also provide some guidelines to design secure ML
models for cognitive networks that are robust to adversarial attacks on the ML
pipeline of cognitive networks.
