Smart-SIEM adds context-aware ML profiling to Wazuh SIEM, lifting binary attack detection F1 to 0.967 and six-class categorization to 0.914 while recovering from concept drift via retraining.
V ., Ershov, V ., & Gulin, A
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
abstract
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.
representative citing papers
KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.
Pith review generated a malformed one-line summary.
Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.
Donor-aware benchmarks show AUROCs up to 0.978 for IBD classification from scRNA-seq using CLR cell-type compositions and GatedStructuralCFN embeddings, with compartment stratification improving both performance and feature stability.
Hybrid predictive modeling of crash data identifies non-use of restraints as the primary risk factor for severe injury in collisions involving trees.
citing papers explorer
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Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profiling
Smart-SIEM adds context-aware ML profiling to Wazuh SIEM, lifting binary attack detection F1 to 0.967 and six-class categorization to 0.914 while recovering from concept drift via retraining.
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Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance
KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.
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Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models
The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.
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Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue
Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.
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Donor-Aware scRNA-seq Benchmarks for IBD Classification
Donor-aware benchmarks show AUROCs up to 0.978 for IBD classification from scRNA-seq using CLR cell-type compositions and GatedStructuralCFN embeddings, with compartment stratification improving both performance and feature stability.
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From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
Hybrid predictive modeling of crash data identifies non-use of restraints as the primary risk factor for severe injury in collisions involving trees.