Cooperative Jamming Detection Using Low-Rank Structure of Received Signal Matrix

2025-07-27

AI Summary

Detecting malicious jamming attacks in cooperative perception wireless networks remains challenging due to incomplete node statistics, unknown channel states, and uncertain noise variance. Method: This paper proposes a novel collaborative jamming detector leveraging the low-rank structure of the received signal matrix combined with a likelihood ratio test (LRT). It models legitimate communication signals via low-rank approximation, synthesizes reference signals, and employs Monte Carlo–based threshold calibration to ensure robustness under practical uncertainties. An analytical expression for the false alarm probability is derived to enable dynamic threshold optimization. Results: Experiments demonstrate that the proposed method significantly outperforms existing approaches across diverse jamming scenarios, achieving high detection accuracy, strong robustness to environmental uncertainties, and low false alarm rates—thereby enhancing the security and reliability of cooperative perception networks.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Wireless communication can be simply subjected to malicious attacks due to its open nature and shared medium. Detecting jamming attacks is the first and necessary step to adopt the anti-jamming strategies. This paper presents novel cooperative jamming detection methods that use the low-rank structure of the received signal matrix. We employed the likelihood ratio test to propose detectors for various scenarios. We regarded several scenarios with different numbers of friendly and jamming nodes and different levels of available statistical information on noise. We also provided an analytical examination of the false alarm performance of one of the proposed detectors, which can be used to adjust the detection threshold. We discussed the synthetic signal generation and the Monte Carlo (MC)-based threshold setting method, where knowledge of the distribution of the jamming-free signal, as well as several parameters such as noise variance and channel state information (CSI), is required to accurately generate synthetic signals for threshold estimation. Extensive simulations reveal that the proposed detectors outperform several existing methods, offering robust and accurate jamming detection in a collaborative network of sensing nodes.

Problem

• Detects jamming attacks using low-rank signal matrix
• Evaluates detectors in varied node and noise scenarios
• Outperforms existing methods with robust collaborative detection

Innovation

• Uses low-rank structure of signal matrix
• Employs likelihood ratio test detectors
• Monte Carlo-based threshold setting method