Results for “Network Intrusion Detection” — Page 3

NASA NTRS2020

POEMMA’s Target-of-Opportunity Sensitivity to Cosmic Neutrino Transient Sources

We investigate the capability of the Probe Of Extreme Multi-Messenger Astrophysics (POEMMA)in performing Target-of-Opportunity (ToO) neutrino observations. POEMMA is a proposed space-based probe-class mission for ultra high-energy cosmic ray and very-high-energy neutrino detection using two spacecraft, each equipped with a large Schmidt telescope to detect optical and near-ultraviolet signals generated by extensive air showers (EASs). POEMMA will be sensitive to Cherenkov radiation from upward-moving EASs initiated by tau neutrinos interacting in the Earth. POEMMA will be able to quickly re-point (90◦in 500 s) each of the two spacecraft to the direction of an astrophysical source, which in combination with its orbital speed will provide it with unparalleled capability to follow up transient alerts. We calculate POEMMA’s transient sensitivity for two observational configurations for the satellites (ToO-stereo and ToO-dual for smaller and larger satellite separations, respectively) and investigate the impact of variations arising due to POEMMA’s orbital characteristics on its sensitivity to tau neutrinos in various regions of the sky. We explore separate scenarios for long (∼105−6s) and short (∼103s) duration events, accounting for intrusion from the Sun and the Moon in the long-duration scenario. We compare the sensitivity and sky coverage of POEMMA for ToO observations with those for existing experiments (e.g., IceCube, ANTARES, and the Pierre Auger Observatory) and other proposed future experiments (e.g., GRAND200k).For long bursts, we find that POEMMA will provide a factor of&7 improvement in average neutrino sensitivity above 300 PeV with respect to existing experiments, reaching the level of model predictions for neutrino fluences at these energies and above from several types of long-duration astrophysical transients (e.g., binary neutron star mergers and tidal disruption events). For short bursts, POEMMA will improve the sensitivity over existing experiments by at least an order of magnitude forEν∼>100 PeV in the “best-case” scenario. POEMMA’s orbital characteristics and rapid re-pointing capability will provide it access to the full celestial sky, including regions that will not be accessible to ground-based neutrino experiments. Finally, we discuss the prospects for POEMMA to detect neutrinos from candidate astrophysical neutrino sources in the nearby universe. Our results demonstrate that with its improved neutrino sensitivity at ultra-high energies and unique full-sky coverage, POEMMA will be an essential, complementary component in a rapidly expanding multi-messenger network.

Tonia M Venters↗

DOE OSTI2026

Feature Engineering and Ensemble Methods for Imbalanced ICS Intrusion Detection: Pipeline Audit and Constrained Evaluation

Industries are becoming increasingly connected and are more vulnerable to cyberattacks due to the widened attack surface. Industrial Control Systems (ICS) are among the most critical sectors that malicious actors can target, as such attacks can cause significant operational disruption and physical damage. It is imperative to detect such attacks as early as possible. This paper evaluates constraint-conditioned optimistic performance estimates for traditional ML models in ICS intrusion detection (i.e., estimates obtained under contiguous, non-shuffled temporal evaluation without test-set alteration, but with pre-split feature engineering that may introduce temporal leakage, due to dataset constraints). Our findings are threefold. First, we quantify how iterative feature engineering affects tree-based ensemble performance and examine how pipeline decisions (split strategy, sampling scope, and cleaning policy) can inflate or reduce reported IDS results under constraint-bound evaluation. Second, we compare intrinsic class-imbalance handling across ensemble models. Third, under our current pipeline constraints (including pre-split feature engineering), CatBoost achieves the best performance on Water Storage Tank (accuracy: 0.9831, class-1 F1: 0.9682), while Light- GBM achieves the best performance on Gas Pipeline (accuracy: 0.9618, class-1 F1: 0.9086).

97 MATHEMATICS AND COMPUTING↗