SearcharxivSearch

arXiv subjects

Daniel Benatar

Publications and source records attributed to Daniel Benatar.

2 recordsLinked to original sources

The leading nuclear-structure electrostatic correction in arbitrary $\beta$ decays

We develop a systematic theoretical framework to improve theoretical predictions for nuclear $\beta$ decays of arbitrary angular momentum $J$, leading to a model-independent nuclear-structure electrostatic correction to the Coulomb interaction between the emitted lepton and the nuclear charge distribution, useful for ongoing and future precision searches for physics beyond the Standard Model. The formalism is based on nuclear matrix elements expanded in multipole operators, as commonly used in \emph{ab initio} calculations. First-order Coulomb corrections are derived from one-photon exchange preserving the full multipole and angular structure of the decay rate, and are subsequently expanded in the relevant small parameters of the nuclear problem, suppressing the leading nuclear structure correction to a few per-mills for medium mass nuclei with natural beta decay properties. We show that within this formalism, the leading Coulomb correction originates from three modifications of the original weak-only interaction: a modification of the nuclear charge form factor, which yields a correction similar to the known Fermi function, a shift of the momentum transfer within the lepton traces, and the same shift but inside the nuclear multipole operators. We additionally provide explicit results for allowed Gamow--Teller and unique first-forbidden transitions.

nucl-th

D-Score: An Expert-Based Method for Assessing the Detectability of IoT-Related Cyber-Attacks

IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting such attacks using network traffic analysis, it has been shown that some attack scenarios are not always equally easy to detect if they involve different IoT models. That is, when targeted at some IoT models, a given attack can be detected rather accurately, while when targeted at others the same attack may result in too many false alarms. In this research, we attempt to explain this variability of IoT attack detectability and devise a risk assessment method capable of addressing a key question: how easy is it for an anomaly-based network intrusion detection system to detect a given cyber-attack involving a specific IoT model? In the process of addressing this question we (a) investigate the predictability of IoT network traffic, (b) present a novel taxonomy for IoT attack detection which also encapsulates traffic predictability aspects, (c) propose an expert-based attack detectability estimation method which uses this taxonomy to derive a detectability score (termed `D-Score') for a given combination of IoT model and attack scenario, and (d) empirically evaluate our method while comparing it with a data-driven method.

cs.CR