Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches
Joshua H. Tyler, Mohamed K.M. Fadul, Matthew R. Hilling, Donald R., Reising, T. Daniel Loveless

TL;DR
This paper investigates the vulnerability of Specific Emitter Identification (SEI) systems to adversarial attacks using deep learning and software-defined radios, demonstrating that such mimicry is feasible but can be mitigated with specific countermeasures.
Contribution
It reveals how DL and SDRs can enable emitter mimicry in SEI, and evaluates countermeasures that hinder adversary success in realistic scenarios.
Findings
DL algorithms and SDRs enable SEI mimicry.
Countermeasures like decoy preambles and denoising autoencoders reduce success.
SWaP-C constraints hinder adversary effectiveness.
Abstract
Specific Emitter Identification (SEI) detects, characterizes, and identifies emitters by exploiting distinct, inherent, and unintentional features in their transmitted signals. Since its introduction, a significant amount of work has been conducted; however, most assume the emitters are passive and that their identifying signal features are immutable and challenging to mimic. Suggesting the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures; however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples, and Software-Defined Radios (SDRs) can manipulate them. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers…
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Taxonomy
TopicsWireless Signal Modulation Classification · Electrostatic Discharge in Electronics · Integrated Circuits and Semiconductor Failure Analysis
