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

Electromagnetic side-channel analysis (EM-SCA) exploits unintended electromagnetic emissions from cryptographic devices to recover secret keys without physical tampering. Since Gandolfi’s seminal 2001 demonstration on smart cards, the field has evolved from simple power analysis to sophisticated correlation-based and template attacks, and more recently, deep learning-based profiling. AES-128 running on embedded microcontrollers remains the canonical target due to its widespread use in IoT and critical infrastructure. Despite advances, nearly all prior work assumes a near-field probe placed millimeters to centimeters from the device, relying on physical proximity as a security barrier. However, the advent of commercial drones equipped with software-defined radios and low-noise amplifiers challenges this assumption, enabling attackers to approach targets at standoff distances of up to 1.5 meters. This evolution necessitates new frameworks that address multi-node aerial signal acquisition, synchronization, and advanced signal processing to realize practical remote EM-SCA.

The core challenge lies in overcoming the limitations of traditional EM-SCA methods that rely on single, near-field probes. Masking countermeasures randomize intermediate computations with fresh masks, requiring second-order analysis that jointly observes mask loading and masked computation leakages. Single-probe systems must algorithmically separate these events from one trace, a process highly sensitive to timing jitter and noise. Introducing drones as mobile collectors adds complexity: hover-induced vibration causes temporal misalignment; electromagnetic interference from drone motors degrades signal quality; and multi-node synchronization is critical for coherent combining. Thus, the problem is to design a multi-drone system capable of spatially decomposed leakage acquisition, precise inter-drone synchronization, autonomous repositioning, and robust second-order mask cancellation to enable effective standoff EM-SCA.

TriSweep introduces several key innovations:

1) Four-Drone Architecture: Three collector drones specialize in distinct leakage windows—Anchor for full-spectrum, Mask Probe for mask-register loading leakage, and Cipher Probe for masked SubBytes output leakage—feeding a stationary Accumulator drone that performs coherent combining and second-order mask cancellation. This spatial decomposition mitigates timing jitter sensitivity inherent in single-probe approaches.

2) Sub-Nanosecond Synchronization: Combining GPS-disciplined oscillators with visual-inertial odometry achieves inter-drone synchronization below 10 nanoseconds, enabling coherent IQ signal fusion critical for SNR enhancement.

3) Distributed Fisher Information Maximization: A 200 ms cycle optimization protocol autonomously adjusts drone positions to maximize information gain, balancing signal quality and operational constraints.

4) Centered Product-Based Second-Order Mask Cancellation: The Accumulator computes the centered product of Mask and Cipher Probe signals, cancelling the mask without requiring mask value knowledge or complex preprocessing.

5) Dual-Channel CNN Integration: A two-channel convolutional neural network in the Accumulator enhances attack robustness on desynchronized datasets, complementing classical template attacks.

• �� System Architecture:
• Drone A (Anchor): Captures full-spectrum EM leakage and coordinates swarm communication.
• Drone B (Mask Probe): Targets mask-register loading leakage window.
• Drone C (Cipher Probe): Targets masked SubBytes output leakage window.
• Drone D (Accumulator): Fixed position, performs coherent combining and second-order mask cancellation.

• �� Hardware Setup:
• Collector drones equipped with USRP B210 SDRs (250 MHz bandwidth, 25 MS/s sampling), Raspberry Pi 5 for processing, GALI-84 LNAs, and Intel RealSense T265 VIO for precise positioning.
• Accumulator drone stationary at ≥2 meters, no SDR payload.

• �� Communication & Synchronization:
• 5 GHz Wi-Fi mesh network for data forwarding.
• Two-stage synchronization: GPSDO for ±1 µs coarse alignment; 1 kHz pilot tone cross-correlation for <10 ns fine alignment.

• �� Target Detection & Localization:
• Each collector drone independently scans frequency spectrum.
• Ground station consensus and time-difference-of-arrival (TDOA) localization via hyperbolic least squares.

• �� Swarm Repositioning:
• Distributed Fisher information maximization over discretized hemispherical candidate positions every 200 ms.
• Anchor drone dispatches waypoints to Mask and Cipher Probes.

• �� Signal Processing & Attack:
• Coherent combining with maximum ratio combining (MRC) weighting.
• Centered product computation for second-order mask cancellation.
• Vectorized template attack with principal-subspace POI selection.
• Two-channel CNN_best architecture with five convolutional layers and two fully connected layers, trained with Adam optimizer over 300 epochs.

Experiments utilize three ANSSI ASCAD datasets: the primary ATmega8515 masked AES-128 dataset and two desynchronized variants with ±50 and ±100 sample jitters simulating hover vibration. A physics-based additive white Gaussian noise model calibrated to free-space path loss simulates standoff distances from 0.25 to 1.5 meters. Key metrics include key rank, reflecting the number of traces required to recover the correct key. Ablation studies compare single-, three-, and four-drone configurations. Profiling-trace cross-correlation alignment is applied to compensate timing jitter. CNN training uses 50,000 profiling traces with batch size 512 on Tesla T4 GPU. Results are averaged over five random seeds to ensure statistical validity.

Simulation results demonstrate that the four-drone system achieves a key rank of 18±1.7 on the ASCAD_Masked dataset at 0.25 meters, a nearly tenfold improvement over the single-drone baseline rank of 197. Cross-correlation alignment reduces the single-drone rank from 89 to 21 on the ASCAD_Desync100 dataset, effectively mitigating timing jitter. Three-drone coherent combining yields approximately +4.8 dB SNR gain, enhancing attack efficiency. The dual-channel CNN converges to a loss of 0.454 and improves key rank to 26 on desynchronized datasets, indicating robustness to temporal misalignment. Cross-dataset drone combining experiments reveal the necessity of matched profiling templates for effective second-order cancellation. Increasing standoff distance results in SNR degradation, limiting effective attack range to about 1.5 meters.

TriSweep’s framework is applicable for security researchers and adversaries aiming to perform remote EM side-channel attacks on embedded cryptographic devices, particularly those employing masking countermeasures. It informs physical security assessments of critical infrastructure, IoT devices, and industrial control systems by highlighting vulnerabilities to aerial platforms. The system’s autonomous swarm coordination and advanced synchronization protocols also have potential applications in wireless signal acquisition, environmental sensing, and cooperative spectrum monitoring. Furthermore, insights from this work can guide regulatory bodies in developing countermeasures against drone-based physical attacks.

The simulation assumes ideal free-space path loss and independent additive noise, neglecting drone body occlusion, multipath propagation, and structured electromagnetic interference from drone motors, which may degrade real-world SNR. Maintaining sub-nanosecond synchronization under hovering vibration and environmental noise remains unproven on physical platforms, posing a significant implementation challenge. The CNN model exhibits overfitting due to limited cross-validation and regularization, limiting generalization to unseen attack traces. The current swarm repositioning algorithm simplifies drone dynamics and collision avoidance, requiring more sophisticated trajectory planning for real deployments.