  Internet-of-Things (IoT) devices that are limited in power and processing are
susceptible to physical layer (PHY) spoofing (signal exploitation) attacks
owing to their inability to implement a full-blown protocol stack for security.
The overwhelming adoption of multicarrier techniques such as orthogonal
frequency division multiplexing (OFDM) for the PHY layer makes IoT devices
further vulnerable to PHY spoofing attacks. These attacks which aim at
injecting bogus/spurious data into the receiver, involve inferring transmission
parameters and finding PHY characteristics of the transmitted signals so as to
spoof the received signal. Non-contiguous (NC) OFDM systems have been argued to
have low probability of exploitation (LPE) characteristics against classic
attacks based on cyclostationary analysis, and the corresponding PHY has been
deemed to be secure. However, with the advent of machine learning (ML)
algorithms, adversaries can devise data-driven attacks to compromise such
systems. It is in this vein that PHY spoofing performance of adversaries
equipped with supervised and unsupervised ML tools are investigated in this
paper. The supervised ML approach is based on deep neural networks (DNN) while
the unsupervised one employs variational autoencoders (VAEs). In particular,
VAEs are shown to be capable of learning representations from NC-OFDM signals
related to their PHY characteristics such as frequency pattern and modulation
scheme, which are useful for PHY spoofing. In addition, a new metric based on
the disentanglement principle is proposed to measure the quality of such
learned representations. Simulation results demonstrate that the performance of
the spoofing adversaries highly depends on the subcarriers' allocation
patterns. Particularly, it is shown that utilizing a random subcarrier
occupancy pattern secures NC-OFDM systems against ML-based attacks.
