ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...

A recent Physical Review Letters publication presents a thorough analysis of MicroBooNE detector data, investigating the anomalous surplus of neutrino-like events detected by the preceding MiniBooNE ...

Exabeam, a global leader in intelligence and automation that powers security operations, and Cribl, the Data Engine for IT and Security, are expanding their strategic partnership—building on their ...

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...

Exabeam, a global leader in intelligence and automation that powers security operations, and Cribl, the Data Engine for IT and Security, today announced an evolution of their strategic partnership ...

This project is designed to identify unusual patterns and potential faults in time-series data (e.g., server metrics, sensor readings, financial data). It goes beyond simple anomaly scoring by: The ...

A comprehensive Python-based machine learning solution for detecting anomalies in multivariate time series data from industrial IoT sensors. This solution identifies abnormal behavior patterns and ...

Introduction: Recent advances in artificial intelligence have created opportunities for medical anomaly detection through multimodal learning frameworks. However, traditional systems struggle to ...

In the fast-changing digital world, cybersecurity threats create substantial risks for organizations globally. Proactive defense is vital to counter these challenges. Real-time vulnerability detection ...