Spatial-temporal beam spoofing (STBS) poses a severe security threat to integrated sensing and communication (ISAC) systems by deliberately manipulating signal properties to fabricate deceptive target echoes, thereby undermining the sensing accuracy and evading the current security measures through the spatial masking and asynchronous injection. To combat this intelligent attack in ISAC, we proposes a beam consistency anomaly detection (BCAD) method, which establishes a physics-constrained verification procedure based on inherent propagation properties of legitimate signals. The proposed BCAD method systematically incorporates essential signal consistency requirements, such as array steering vector coherence, Doppler shift linearity, and phase progression consistency, into multivariate polynomial formulations. It then utilizes sum-of-squares relaxation to rigorously verify the global non-negativity of these polynomials across the entire parameter space encompassing angle-of-arrival, delay, and Doppler shift. This verification process confirms the physical legitimacy of the signal, and a negative result reveals violations of the transmission continuity constraint caused by STBS. Numeric results are presented to show the detection performance of the proposed BCAD method.

In this paper, we explore the vulnerability of the physical uplink shared channel (PUSCH) to a new smart jamming attack in fifth generation (5G) new radio (NR), where an intelligent adversary first executes its attack by sniffing the downlink control information (DCI)-indicated resource scheduling information and then disrupts the PUSCH data transmission effectively and covertly by the precise jamming. To combat such kind of DCI sniffing based smart jamming (DCIS-SJ), we propose a novel method for effective DCIS-SJ suppression leveraging the DCI-scheduled subset identification and the PUSCH resource reconstruction. Our method fundamentally relies on the differences in the spatial domain feature under available control channel elements and resource block group granularities between legitimate users and the DCIS-SJ attacker, to selectively exclude unwanted elements while safeguarding the authenticity of the targeted transmissions. Numerical results evaluate and confirm the effectiveness of our method.

In the passive bistatic radar (PBR) system, there exists methods used to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delayDoppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model results in a reduction of the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results using digital video broadcasting-terrestrial (DVB-T) signal indicate that the proposed approach enables fast and stable detection of weak targets.