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Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT

机译:学习辅助物理层攻击IOT中的多载波通信

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Internet-of-Things (IoT) devices that are limited in power and processing capabilities 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 estimation/classification utilizing 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 used at the transmitter. Particularly, it is shown that utilizing a random subcarrier occupancy pattern precludes the adversary from spoofing and secures NC-OFDM systems against ML-based attacks.
机译:由于其无法实现安全性的协议栈而受到电力和处理能力的信息,这些内容(IOT)的设备易受物理层(PHY)欺骗(信号开发)攻击影响。为PHY层的正交频分复用(OFDM)等多次载波技术的压倒性地采用了IOT设备进一步容易受到PHY欺骗攻击的影响。这些攻击旨在将虚假/虚假数据注入接收器,涉及推断传输参数并找到发射信号的PHY特性,以便欺骗接收的信号。已经认为非连续(NC)OFDM系统具有基于裂纹分析的经典攻击的剥削(LPE)特征的低概率,并且相应的PHY被认为是安全的。然而,随着机器学习(ML)算法的出现,对手可以设计数据驱动的攻击来损害这种系统。在这篇文章中,这是在这方面,在本文中调查了配备有监督和无监督的ML工具的对手的PHY欺骗对手的性能。监督ML方法基于利用深神经网络(DNN)的估计/分类,而无监督的人使用变分性AutiCencoders(VAES)。特别地,VAE被证明能够从与其PHY特性相关的NC-OFDM信号学习诸如频率模式和调制方案的NC-OFDM信号,这对于PHY欺骗是有用的。此外,提出了一种基于解剖原则的新度量,以衡量这些学习象征的质量。仿真结果表明,欺骗对手的性能高度取决于发射机中使用的子载波的分配模式。特别地,示出利用随机子载波占用模式,从欺骗和保护NC-OFDM系统免受基于ML的攻击来阻止对手。

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