TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis

Passive acoustic sensing is an attractive modality for counter-unmanned aerial system (counter-UAS) defence: it is covert, low-cost, and effective against drones with small radar cross-sections or minimal radio emissions. We present EchoHawk, an open and fully reproducible reference pipeline that detects a drone from its rotor harmonics, estimates its blade-passing frequency, and localises it with a microphone array via classical wideband beamforming (delay-and-sum, MVDR, MUSIC) and time-delay processing (GCC-PHAT, SRP-PHAT), followed by temporal tracking. We evaluate the system on a physically transparent synthetic benchmark that pits drones against hard low-frequency harmonic confusers, such as ground vehicles, and on real recorded audio. Our central methodological contribution is a documented case of session-level data leakage in a widely used public dataset: because its recordings are pre-segmented into short clips, naive clip-level splits place adjacent slices of the same continuous recording in both training and test sets, inflating reported performance. Enforcing recording-session-grouped cross-validation reduces, for example, a random-forest baseline's detection probability at a 1% false-alarm rate from 0.796 to 0.745, yielding honest numbers. All code, figures, and a synthetic data generator are released so that every result runs without any download.

Deep learning-based side-channel analysis has historically focused on single-byte targets and manually cropped traces, which risks discarding exploitable leakage. While recent work has proposed specialized architectures and resampling techniques to address this gap, the literature lacks a simple transformer baseline for simultaneous full-key attacks on uncropped traces. We present an open-source transformer implementation for uncropped full-key attacks which uses the standard transformer encoder backbone, adapting only the input and output layers to the side-channel setting. We release our implementation, training recipes, and pretrained weights for uncropped ASCADv1f, ASCADv1r, and CHES-CTF-2018 which achieve performance competitive with previously-reported results, while using less than 10GB of VRAM and requiring at most 3.34 hours of training on a single NVIDIA A6000.

The initial development phase of UAV swarms largely depends on simulation for experimental design and validation, yet existing open-source tools are often unmaintained, have steep learning curves, or are built around a single fixed scenario. The need for a comprehensive, modular simulation platform is a recognized research gap. This paper presents SwarmFly, a MATLAB-based simulation and test platform for multi- UAV swarms that addresses these gaps. SwarmFly combines a real-time operational map, four swarm coordination modes (leader-follower, decentralized, heterogeneous relay, and heterogeneous speed), simulated IMU telemetry, and IP-based geolocation with a plugin architecture that lets researchers add behaviors, fault models, and analysis tools without touching the core code. Eight bundled plugins extend the base simulator into a full test harness. The SwarmFly platform exposes multi-agent aerial swarms to a wide range of internal and external disruptions, enabling observation and quantification of underlying swarm control and behavioral mechanisms. This study verifies and characterizes each subsystem through eight experiments that measure formation accuracy, wind tolerance, fault recovery, energy endurance, and airspace compliance. The platform runs entirely in MATLAB. Its modular design supports straightforward extension toward hardware-in-the-loop testing, larger swarms, and higher-fidelity dynamics. An open-source release is available at [https://github.com/abhishekphadke/SwarmFly.git]