# Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT

## TL;DR

This paper investigates how machine learning algorithms, including deep neural networks and variational autoencoders, can be used to perform physical layer spoofing attacks on multicarrier IoT communications, revealing vulnerabilities and proposing mitigation strategies.

## Contribution

It introduces ML-based PHY spoofing methods for NC-OFDM systems and proposes a new metric for evaluating learned representations, highlighting security vulnerabilities.

## Key findings

- VAEs can learn PHY characteristics for spoofing
- Random subcarrier patterns improve security against ML attacks
- Spoofing performance depends on subcarrier allocation

## Abstract

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.