Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth

• URL: http://arxiv.org/abs/2210.02897v2
• Date: Wed, 19 Apr 2023 12:57:50 GMT
• Title: Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth
• Authors: Anu Jagannath and Jithin Jagannath
• Abstract summary: We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. The proposed Mbed-ATN framework results in a 5.32x higher TPR, 37.9% fewer false alarms, and 6.74x higher accuracy under the challenging real-world setting.
• Score: 1.218340575383456
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its generalization capability is analyzed in different settings and the effect of sample length and anti-aliasing decimation is demonstrated. The embedding module serves as a dimensionality reduction unit that maps the high dimensional 3D input tensor to a 1D feature vector for further processing by the ATN module. Furthermore, unlike the prior research in this field, we closely evaluate the complexity of the model and test its fingerprinting capability with real-world Bluetooth dataset collected under a different time frame and experimental setting while being trained on another. Our study reveals a 9.17x and 65.2x lesser memory usage at a sample length of 100 kS when compared to the benchmark - GRU and Oracle models respectively. Further, the proposed Mbed-ATN showcases 16.9x fewer FLOPs and 7.5x lesser trainable parameters when compared to Oracle. Finally, we show that when subject to anti-aliasing decimation and at greater input sample lengths of 1 MS, the proposed Mbed-ATN framework results in a 5.32x higher TPR, 37.9% fewer false alarms, and 6.74x higher accuracy under the challenging real-world setting.

Related papers

• High-Speed Detector For Low-Powered Devices In Aerial Grasping [16.940649285960028]
  Fast Fruit Detector (FFD) is a resource-efficient, single-stage, and postprocessing-free object detector. FFD achieves 100FPS@FP32 precision on the latest 10W NVIDIA Jetson-NX embedded device.
  arXiv  Detail & Related papers  (2024-02-22T14:44:50Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation [113.35352122662752]
  We present an efficient multi-modal backbone for outdoor 3D perception named UniTR. UniTR processes a variety of modalities with unified modeling and shared parameters. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks.
  arXiv  Detail & Related papers  (2023-08-15T12:13:44Z)
• Efficient Deep Learning Models for Privacy-preserving People Counting on Low-resolution Infrared Arrays [11.363207467478134]
  Infrared (IR) array sensors offer a low-cost, energy-efficient, and privacy-preserving solution for people counting. Previous work has shown that Deep Learning (DL) can yield superior performance on this task. We compare 6 different DL architectures on a novel dataset composed of IR images collected from a commercial 8x8 array.
  arXiv  Detail & Related papers  (2023-04-12T15:29:28Z)
• Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors [64.68349896377629]
  This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to 2m distance.
  arXiv  Detail & Related papers  (2023-03-03T14:14:08Z)
• The case for 4-bit precision: k-bit Inference Scaling Laws [75.4335600212427]
  Quantization methods reduce the number of bits required to represent each parameter in a model. The final model size depends on both the number of parameters of the original model and the rate of compression. We run more than 35,000 zero-shot experiments with 16-bit inputs and k-bit parameters to examine which quantization methods improve scaling for 3 to 8-bit precision.
  arXiv  Detail & Related papers  (2022-12-19T18:48:33Z)
• RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth [1.0312968200748116]
  A novel cross-domain attentional multi-task architecture - xDom - is presented in this work. We resort to real-world IoT WiFi and Bluetooth (BT) emissions in a rich multipath and unavoidable interference environment. We report performance improvements by up to 59.3% and 4.91x under single-task WiFi and BT fingerprinting respectively.
  arXiv  Detail & Related papers  (2022-09-07T13:38:06Z)
• PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices [13.62426382827205]
  PP-PicoDet family of real-time object detectors achieves superior performance on object detection for mobile devices. Models achieve better trade-offs between accuracy and latency compared to other popular models.
  arXiv  Detail & Related papers  (2021-11-01T12:53:17Z)
• YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs [14.85882314822983]
  In order to map deep neural network (DNN) based object detection models to edge devices, one typically needs to compress such models significantly. In this paper, we propose a novel edge GPU friendly module for multi-scale feature interaction. We also propose a novel learning backbone adoption inspired by the changing translational information flow across various tasks.
  arXiv  Detail & Related papers  (2021-10-26T14:02:59Z)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)
• ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection [69.68263074432224]
  We present a novel framework named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes. To further exploit the abundant texture cues in RGB images for more accurate disparity estimation, we introduce a conceptually straight-forward module -- adaptive zooming.
  arXiv  Detail & Related papers  (2020-03-01T17:18:08Z)

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.