Jierui Chen | Searcharxiv

arXiv2026

Precision-Aware Hierarchical Spectrum Search for Finite-Precision MUSIC DOA Estimation

Dense-grid spectrum search is computationally costly for multiple signal classification (MUSIC) direction-of-arrival (DOA) estimation, and low-precision evaluation can introduce numerical errors that distort candidate selection. This paper proposes precision-aware hierarchical MUSIC (PAH-MUSIC), a multistage framework that coordinates arithmetic precision, angular grids, and candidate windows in spectrum evaluation, while covariance estimation and eigendecomposition remain in FP64. Stage-dependent precision is assigned from coarse candidate discovery to final DOA estimation. Under bounded perturbations and local spectral growth, a conditional displacement bound gives a sufficient window-width condition for retained, correctly associated candidates. For a two-stage continuous cost model with fixed precision weights and window coefficients, a closed-form optimal coarse step and a cost-gain condition are derived. Simulations with two separated sources and 500 frames at each tested signal-to-noise ratio (SNR) from -10 to 15 dB show that FP8, FP16, and FP32 coarse searches followed by FP64 refinement achieve DOA root-mean-square errors close to a baseline using FP64 at both search stages. Relative to that baseline, the selected configurations reduce weighted spectrum-evaluation cost by 28.36\%--44.66\% under the adopted precision weights.

eess.SP↗

arXiv2026

What's Your NIC Whispering? Network Threat Behavior Recognition via NIC Electromagnetic Side-Channel Leakage

Conventional network threat detection primarily relies on packet-level, flow-level, or host-level telemetry. This paper investigates a different observation surface: unintended electromagnetic(EM) emissions generated by network interface card(NIC) activity, and asks whether such physical leakage contains sufficiently structured information for network threat-behavior recognition. We present NICWhisper, which externally captures NIC EM emissions, transforms raw measurements into time-frequency representations, and recognizes network behaviors without inspecting packet contents or host-side runtime states. Rather than competing with traffic-based detection, NICWhisper exploits the physical manifestation of traffic-driven NIC activity, whose timing, rate, concurrency, and burst organization naturally shape the measured EM leakage. We construct a NIC EM dataset covering active benign workloads and seven representative threat behaviors under diverse execution conditions, and systematically evaluate signal dependence, execution variation, measurement perturbation, and cross-device transfer. NICWhisper achieves 80.67\% Macro-F1 across eight behavior classes, while further experiments show that the observed behavior-related information extends beyond simple signal magnitude and remains partially transferable across execution conditions and NIC hardware. These results establish NIC EM leakage as a complementary physical observation source for network security monitoring when direct access to conventional traffic or host telemetry is limited or undesirable.

cs.CR↗