This paper proposes a heterogeneous neural network accelerator for multi-task RF signal recognition, achieving high accuracy and low latency for automatic modulation recognition, hardware-Trojan cover…

This paper proposes a physical backdoor attack against deep learning modulation classifiers, utilizing power amplifier non-linear distortions as physical triggers to achieve high attack success rates.

This paper investigates a novel physical backdoor attack against Deep Automatic Modulation Classifiers (AMC) in wireless communications, demonstrating that an adversary using Explainable AI (XAI) can…

A spectrogram-based framework is proposed for event detection, localization, and classification in power system waveforms using short-time Fourier transform.

The paper proposes a theoretically grounded adversarial multi-task learning framework (AMTIDIN) that significantly improves joint interference detection, modulation identification, and interference id…

This paper proposes a physical layer authentication mechanism using integrated sensing and communication (ISAC) to reconstruct environment layout, infer propagation channels, and authenticate transmit…

The paper proposes EnThM, a lightweight, hierarchical verification scheme that uses statistical and rule-based checks on aggregated metering data to mitigate real-time power theft in smart grids.

This paper identifies scattering network architectures that maximize separation capacity for data with low intrinsic dimension by characterizing and bounding the separation capacity of general feature…

The paper proposes a comprehensive application-layer reference monitor to detect and mitigate data exfiltration via covert channels embedded in LLM agent egress payloads across text, image, and audio…

This paper proposes Agon, a semi-supervised satellite interference detection framework using a novel two-stage hybrid learning paradigm, achieving state-of-the-art detection performance with a 25.3% i…

The paper introduces SpikeWFM, a novel hybrid architecture combining spiking neural networks (SNNs) and transformers, which significantly improves the robustness and accuracy of wireless foundation mo…

This paper proposes a unified deep learning framework for joint NBI cancellation and robust soft demodulation using NBI-CNet and LLR-CNet to eliminate error floors and improve performance in OFDM syst…

This paper proposes a cyclic-prefix OFDM system for distributed acoustic sensing (DAS) to eliminate spatial inter-symbol interference and enable shared-waveform integrated sensing and communication.

This paper proposes a vision-assisted OFDM-ISAC framework that fuses wireless and visual modalities to resolve ambiguity in multi-target scenarios and achieve better localization and delay performance…