Sponsor
This work is partially supported by the NSF Award #2451803.
Published In
Wisec 2026 Proceedings of the 19th ACM Conference on Security and Privacy in Wireless and Mobile Networks
Document Type
Conference Proceeding
Publication Date
6-29-2026
Subjects
Bluetooth Low Energy, Anomaly Detection, BLE Trackers
Abstract
Detecting malicious Bluetooth Low Energy (BLE) trackers remains challenging because existing approaches rely on fixed time and distance thresholds that are brittle across environments. These heuristics produce false positives in dense environments and require long observation windows before flagging a device. To address these limitations, we present BL(u)E CRAB, a cross-platform (iOS/Android) mobile system that represents nearby devices using three risk factors derived from BLE scan data. Our detection model adapts Clustering-Based Local Outlier Factor (CBLOF) to BLE tracker detection and adds a gap-thresholding mechanism to separate high-scoring outliers from the benign majority. Across micro-benchmarks and end-to-end case studies, CBLOF reduces false positives up to 77% and false negatives up to 20% compared to the state of the art. In our case studies, suspicious trackers are typically detected within 5 minutes of scanning, improving practical usability for real-world deployment.
Rights
Copyright (c) 2026 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1145/3765613.3811690
Persistent Identifier
https://archives.pdx.edu/ds/psu/45204
Citation Details
Ekhator, O., Conklin, D., Pappachan, P., & Yus, R. (2026). Towards Fast Detection of Suspicious Bluetooth Trackers using Anomaly Detection. In (Editor), Proceedings of the 19th ACM Conference on Security and Privacy in Wireless and Mobile Networks.