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.

中文翻译：

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物联网中针对多载波通信的学习辅助物理层攻击

功率和处理能力有限的物联网 (IoT) 设备容易受到物理层 (PHY) 欺骗（信号利用）攻击，因为它们无法实施完整的协议栈以确保安全。物理层大量采用正交频分复用 (OFDM) 等多载波技术使物联网设备更容易受到 PHY 欺骗攻击。这些攻击旨在将伪造/虚假数据注入接收器，包括推断传输参数和发现传输信号的 PHY 特性，以欺骗接收信号。非连续 (NC) OFDM 系统被认为具有针对基于循环平稳分析的经典攻击的低利用概率 (LPE) 特性，并且相应的 PHY 已被认为是安全的。然而，随着机器学习 (ML) 算法的出现，攻击者可以设计数据驱动的攻击来破坏此类系统。正是在这种情况下，本文研究了配备有监督和无监督 ML 工具的对手的 PHY 欺骗性能。有监督的机器学习方法基于深度神经网络 (DNN)，而无监督的机器学习方法采用变分自编码器 (VAE)。特别是，VAE 被证明能够从 NC-OFDM 信号中学习与其 PHY 特性相关的表示，例如频率模式和调制方案，这对于 PHY 欺骗很有用。此外，提出了一种基于解缠结原理的新度量来衡量此类学习表示的质量。仿真结果表明，欺骗对手的性能高度依赖于子载波的分配模式。特别是，它表明利用随机子载波占用模式可以保护 NC-OFDM 系统免受基于 ML 的攻击。