AirKey: Multimodal Acoustic-Assisted WiFi Sensing for Zero-Training Robust PIN Inference

• URL: http://arxiv.org/abs/2608.03151v1
• Date: Tue, 04 Aug 2026 05:30:17 GMT
• Title: AirKey: Multimodal Acoustic-Assisted WiFi Sensing for Zero-Training Robust PIN Inference
• Abstract summary: Contactless keystroke inference via WiFi sensing highlights severe privacy threats.<n>AirKey is a novel cross-modal sensing framework that achieves eavesdropping.<n>AirKey achieves over 4x higher accuracy than state-of-the-art unimodal zero-training schemes.
• Score: 18.673405698977145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contactless keystroke inference via WiFi sensing highlights severe privacy threats, yet its real-world feasibility is hindered by two fundamental physical and deployment bottlenecks: the strict requirement for network privileges to acquire stable sensing streams, and the inherent "waveform fusion" ambiguity of pure WiFi signals during rapid, muscle-memory typing. To overcome these limitations, we propose AirKey, a novel cross-modal sensing framework that achieves highly stealthy, zero-training PIN eavesdropping. First, to bypass network deployment barriers, AirKey exploits fundamental IEEE 802.11 mechanisms to predictably elicit Acknowledgment (ACK) responses from unmodified target devices. By passively harvesting Channel State Information (CSI) from these ACKs using a low-cost microcontroller, AirKey secures a continuous spatial sensing stream entirely without network association. Crucially, to resolve the WiFi waveform fusion bottleneck, AirKey introduces a cross-modal complementarity mechanism. By utilizing lightweight acoustic signals as precise temporal anchors, the system robustly guides the segmentation of overlapping CSI trajectories. This joint spatiotemporal fusion strictly intersects CSI-derived spatial similarities with acoustic-guided inter-keystroke timing. Extensive real-world evaluations demonstrate that AirKey achieves over 4x higher accuracy than state-of-the-art unimodal zero-training schemes, successfully recovering device-unlock PINs within 6 attempts. Ultimately, this work exposes a critical vulnerability in contemporary smart interfaces, underscoring the severe privacy implications of ubiquitous multimodal sensing.

Related papers

• AmbSentry: Mitigating Sensing Eavesdropping in ISAC Systems by Harnessing Ambient IoT Devices [60.083979780396014]
  Integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for 6G networks.<n>This paper proposes AmbSentry, an ISAC system that prevents the leakage of sensing information to sensing eavesdroppers.
  arXiv  Detail & Related papers  (2026-08-12T08:42:05Z)
• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers [69.56534335936534]
  AmbShield is an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers'<n>In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices.
  arXiv  Detail & Related papers  (2026-01-14T20:56:50Z)
• WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism [61.79272554643873]
  We propose a gesture recognition network that integrates a multi-semantic attention mechanism with a self-attention-based channel mechanism.<n>The results show that it not only maintains high in-domain accuracy of 99.72%, but also achieves high performance in cross-domain recognition of 97.61%.
  arXiv  Detail & Related papers  (2025-12-04T07:09:13Z)
• Extracting Range-Doppler Information of Moving Targets from Wi-Fi Channel State Information [1.3581639904351783]
  We propose a new signal processing approach that addresses both challenges via three key innovations.<n>Our method achieves cm-level accuracy in range Doppler estimation of moving targets, validated using a commercial Intel WiFi AX211.
  arXiv  Detail & Related papers  (2025-08-04T18:10:18Z)
• Privacy-Preserving Secure Neighbor Discovery for Wireless Networks [0.0]
  Traditional Neighbor Discovery (ND) and Secure Neighbor Discovery (SND) are key elements for network functionality.<n>We present a novel Privacy-Preserving Secure Neighbor Discovery (PP-SND) protocol, enabling devices to perform SND without revealing their actual identities and locations.
  arXiv  Detail & Related papers  (2025-03-28T08:27:47Z)
• OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
  This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
  arXiv  Detail & Related papers  (2024-06-07T09:20:30Z)
• Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks [18.25251260793266]
  Deep learning technologies are inherently vulnerable to adversarial perturbation attacks. In this paper, we elaborate such an attack, called WiIntruder, distinguishing itself with robustness, universality, and stealthiness. Tests confirm the practical threats of perturbation attacks to common WiFi-based services, including user authentication and respiratory monitoring.
  arXiv  Detail & Related papers  (2024-04-24T01:43:07Z)
• Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security [7.8344795632171325]
  Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality. This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework.
  arXiv  Detail & Related papers  (2024-02-08T00:23:42Z)
• WiFi-based Spatiotemporal Human Action Perception [53.41825941088989]
  An end-to-end WiFi signal neural network (SNN) is proposed to enable WiFi-only sensing in both line-of-sight and non-line-of-sight scenarios. Especially, the 3D convolution module is able to explore thetemporal continuity of WiFi signals, and the feature self-attention module can explicitly maintain dominant features.
  arXiv  Detail & Related papers  (2022-06-20T16:03:45Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)

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.