Developing robust security for industrial control systems requires more than just applying conventional methods, and this review highlights the significant hurdles in achieving truly holistic intrusion detection.
A new class of denial-of-service attacks targets multi-round block builders by leveraging the inherent dependencies between transactions.
This review details a new algorithm for training machine learning models across networks while protecting sensitive data.
![A residual risk score is computed by integrating semantic similarity [latex]S_{sem} [/latex], structural similarity [latex]S_{ast} [/latex], and cross-model agreement [latex] \sigma_{cross}^2 [/latex], offering a comprehensive assessment of potential vulnerabilities.](https://arxiv.org/html/2604.21051v1/x3.png)
New research reveals that many software patches offer only superficial changes, leaving residual risks lurking in seemingly secure code.
Researchers have achieved a complete realization of extended orthosymplectic quantum supergroups, unlocking deeper insights into their intricate mathematical structure.
A new framework systematically exposes developer pitfalls in the security of systems that orchestrate large language model agents.
As 5G networks become increasingly vital, ensuring the integrity of location data is paramount, and this review explores methods to protect against sophisticated spoofing and jamming threats.
![The study demonstrates how constraints progressively refine predictions for the mass-radius relation of neutron stars, narrowing the range of possible equations of state-from those based solely on chiral effective field theory, to those incorporating maximum mass limits [latex]M_{max} \leq 2.16^{+0.17}_{-0.15}[/latex], and finally, those informed by astrophysical observations of pulsars like PSR J0030+0451 and gravitational wave events like GW170817, ultimately revealing which equations of state persist under increasing scrutiny.](https://arxiv.org/html/2604.21039v1/plots/CQR-last.png)
A new approach combines statistical rigor with astrophysical modeling to reliably quantify uncertainties in the elusive equation of state governing neutron stars.
Researchers have developed a novel method for rigorously proving the correctness of programs running on modern processors with relaxed memory access rules.
A new approach to self-supervised learning leverages uncertainty estimation to build more robust representations from aerial imagery, even when data is incomplete or corrupted.