Security Analysis of Time-of-Arrival Estimation via Cross-Correlation under Narrow-Band Conditions
- URL: http://arxiv.org/abs/2605.10632v1
- Date: Mon, 11 May 2026 14:21:37 GMT
- Title: Security Analysis of Time-of-Arrival Estimation via Cross-Correlation under Narrow-Band Conditions
- Abstract summary: We present and analyze two new attacks that reshape signals to compromise correlation-based ToA estimation.<n>First attack multiplies the signal by a symbol-periodic waveform in the time domain, while the second passes it through a negative group delay filter.
- Score: 4.4681678689625715
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Time-of-arrival (ToA) estimation via cross-correlation is an essential building block of time-of-flight ranging. However, in narrowband systems, it is notoriously difficult to protect against distance-decreasing attacks such as Early-Detect/Late-Commit (ED/LC). We present and analyze two new attacks that reshape ranging signals to compromise correlation-based ToA estimation. The first attack multiplies the signal by a symbol-periodic waveform in the time domain, while the second passes it through a negative group delay (NGD) filter. In contrast to ED/LC, our attacks do not require real-time symbol detection or adaptive compensation; they are completely symbol-agnostic. We describe implementation strategies for both attacks and discuss NGD filtering in the context of Bluetooth Channel Sounding (CS), a recent narrowband ranging system. To this end, we simulate an NGD circuit in LTspice and a ToA estimator in MATLAB, demonstrating that the attack can result in distance reductions of up to 18 m against Bluetooth CS RTT ranging. Finally, we verify the feasibility of the NGD approach by building a prototype using commercial off-the-shelf components.
Related papers
- Autonomous Cyber Defense: Real-Time Attack Detection and Mitigation in Software-Defined Networks Using Machine Learning [78.29352305480285]
In recent intrusions, the interval between initial access and the first lateral movement has been observed to be as short as 27 seconds.<n>This paper presents a closed-loop framework that detects and blocks attacks in software-defined networks without operator involvement.
arXiv Detail & Related papers (2026-08-22T18:38:39Z) - Detecting Adversarial Evasion Attacks Against Autoencoder-Based Network Intrusion Detection Systems [0.0]
Evasion attacks deliberately manipulate input to an ML-based system to produce an incorrect prediction while the manipulated input still appears benign.<n>In this paper, we propose two complementary detectors: the Residual Localisation Detector (RLD) and the Feature-Space Perturbation Consistency (FPC) Detector.<n>We evaluate both detectors on benign, malicious, and adversarial traffic from multiple IoT devices in the UQ-IoT dataset.
arXiv Detail & Related papers (2026-07-01T17:26:24Z) - Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models [53.79667447811139]
We show that a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal.<n>These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader estimation.
arXiv Detail & Related papers (2026-02-04T15:25:02Z) - Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks [87.16809558673403]
Spiking neural networks (SNNs) compute with discrete spikes and exploit temporal structure.<n>We study a timing-only adversary that retimes existing spikes while preserving spike counts and amplitudes in event-driven SNNs.
arXiv Detail & Related papers (2026-02-03T09:06:53Z) - Joint single-shot ToA and DoA estimation for VAA-based BLE ranging with phase ambiguity: A deep learning-based approach [21.46227704588283]
Virtual antenna array (VAA) techniques enable DoA estimation with a single antenna, making angle estimation feasible on such devices.<n>We propose a unified model that combines VAA with BLE two-way CFR, and introduce a neural network based phase recovery framework.<n>The proposed method achieves superior performance under non-uniform VAAs, with mean square errors approaching the Cramer Rao bound at SNR $geq$ 5 dB.
arXiv Detail & Related papers (2026-01-21T15:31:52Z) - Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids [2.721477719641864]
This paper introduces a graph-centric, multimodal detector that fuses physical-layer and behavioral indicators over temporal windows to detect passive attacks.<n>The model achieves a testing accuracy of 98.32% per-timestep and 93.35% per-sequence at 0.15% FPR.
arXiv Detail & Related papers (2025-09-29T08:52:30Z) - Adaptive t Design Dummy-Gate Obfuscation for Cryogenic Scale Enforcement [0.0]
Cloud quantum services can reveal circuit structure and timing through scheduler metadata, latency patterns, and co-tenant interference.<n>We introduce NADGO, a scheduling and obfuscation stack that enforces operational privacy for gate-model workloads.<n>We prototype the approach on a 4-qubit superconducting tile with cryo-CMOS control and evaluate both depth-varied local-random circuits and small QAOA instances.
arXiv Detail & Related papers (2025-08-31T12:12:48Z) - WFCAT: Augmenting Website Fingerprinting with Channel-wise Attention on Timing Features [16.443437518731383]
Website Fingerprinting aims to deanonymize users on the Tor network by analyzing encrypted network traffic.<n>Recent deep-learning-based attacks show high accuracy on undefended traces.<n>But they struggle against modern defenses that use tactics like injecting dummy packets and delaying real packets.
arXiv Detail & Related papers (2024-12-16T07:04:44Z) - AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries.
We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots.
We also show that ACPT is robust to 3 types of adaptive attacks.
arXiv Detail & Related papers (2024-08-04T09:53:50Z) - DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data.
It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
arXiv Detail & Related papers (2024-06-05T06:18:03Z) - Concealed Electronic Countermeasures of Radar Signal with Adversarial
Examples [7.460768868547269]
Electronic countermeasures involving radar signals are an important aspect of modern warfare.
Traditional electronic countermeasures techniques typically add large-scale interference signals to ensure interference effects, which can lead to attacks being too obvious.
In recent years, AI-based attack methods have emerged that can effectively solve this problem, but the attack scenarios are currently limited to time domain radar signal classification.
arXiv Detail & Related papers (2023-10-12T12:53:44Z) - DAAIN: Detection of Anomalous and Adversarial Input using Normalizing
Flows [52.31831255787147]
We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA)
Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution.
Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
arXiv Detail & Related papers (2021-05-30T22:07:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.