Anti-Tamper Radio meets Reconfigurable Intelligent Surface for System-Level Tamper Detection
- URL: http://arxiv.org/abs/2503.14279v1
- Date: Tue, 18 Mar 2025 14:18:31 GMT
- Title: Anti-Tamper Radio meets Reconfigurable Intelligent Surface for System-Level Tamper Detection
- Authors: Maryam Shaygan Tabar, Johannes Kortz, Paul Staat, Harald Elders-Boll, Christof Paar, Christian Zenger,
- Abstract summary: We propose and experimentally evaluate an ATR system complemented by an RIS to dynamically reconfigure the wireless propagation environment.<n>We show that this approach can enhance resistance against signal manipulation attacks, reduce bandwidth requirements from severalGHz down to as low as 20 MHz, and improve robustness to environmental disturbances such as internal fan movements.
- Score: 5.158378873123946
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many computing systems need to be protected against physical attacks using active tamper detection based on sensors. One technical solution is to employ an ATR (Anti-Tamper Radio) approach, analyzing the radio wave propagation effects within a protected device to detect unauthorized physical alterations. However, ATR systems face key challenges in terms of susceptibility to signal manipulation attacks, limited reliability due to environmental noise, and regulatory constraints from wide bandwidth usage. In this work, we propose and experimentally evaluate an ATR system complemented by an RIS to dynamically reconfigure the wireless propagation environment. We show that this approach can enhance resistance against signal manipulation attacks, reduce bandwidth requirements from several~GHz down to as low as 20 MHz, and improve robustness to environmental disturbances such as internal fan movements. Our work demonstrates that RIS integration can strengthen the ATR performance to enhance security, sensitivity, and robustness, recognizing the potential of smart radio environments for ATR-based tamper detection
Related papers
- PCA-Featured Transformer for Jamming Detection in 5G UAV Networks [0.5999777817331317]
Unmanned Aerial Vehicles (UAVs) face significant security risks from jamming attacks, which can compromise network functionality.<n>Traditional detection methods often fall short when confronting AI-powered jamming that dynamically modifies its behavior.<n>We introduce a novel U-shaped transformer architecture to refine feature representations for improved wireless security.
arXiv Detail & Related papers (2024-12-19T16:13:04Z) - Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance [78.76421728334013]
Rydberg atomic quantum receivers (RAQRs) are an eminent solution for detecting the electric field of radio frequency (RF) signals.<n>We introduce the superheterodyne version of RAQRs to the wireless community by presenting an end-to-end reception scheme.<n>We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow.
arXiv Detail & Related papers (2024-12-07T06:25:54Z) - On the Potential of Re-configurable Intelligent Surface (RIS)-assisted Physical Layer Authentication (PLA) [0.0]
Re-configurable Intelligent Surfaces (RIS) technology is increasingly becoming a potential component for next-generation wireless networks.
However, the broadcast nature of RIS-assisted wireless communication makes it vulnerable to malicious attacks at the physical layer.
This paper investigates RIS-assisted wireless communication systems to unlock the potential of using RIS for physical layer authentication (PLA)
arXiv Detail & Related papers (2024-05-01T10:17:24Z) - FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks.
We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness.
Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
arXiv Detail & Related papers (2024-03-26T08:51:23Z) - DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for
CAVs [11.15939066175832]
jamming attacks pose substantial risks to the 5G network.
This work presents a novel deep learning-based technique for detecting jammers in CAV networks.
Results show that the proposed method achieves 96.4% detection rate in extra low jamming power.
arXiv Detail & Related papers (2024-03-05T04:29:31Z) - Enhancing Reliability in Federated mmWave Networks: A Practical and
Scalable Solution using Radar-Aided Dynamic Blockage Recognition [14.18507067281377]
This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments.
In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles.
The proposed approach, coined as Radar-aided blockage Dynamic Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model.
arXiv Detail & Related papers (2023-06-22T10:10:25Z) - Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User
Experiences [118.91584633024907]
A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
arXiv Detail & Related papers (2023-04-29T00:39:50Z) - 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) - Covert Communications via Adversarial Machine Learning and
Reconfigurable Intelligent Surfaces [46.34482158291128]
The reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control the scattering and reflection profiles of signals.
In this paper, covert communication is considered in the presence of the RIS.
arXiv Detail & Related papers (2021-12-21T18:23:57Z) - Ultra-Reliable Indoor Millimeter Wave Communications using Multiple
Artificial Intelligence-Powered Intelligent Surfaces [115.85072043481414]
We propose a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs)
The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP)
Two centralized and distributed controllers are proposed to control the policies of the mmW AP and RISs.
arXiv Detail & Related papers (2021-03-31T19:15:49Z)
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.