IEEE

Benchmarking of Machine Learning for Anomalybased Intrusion Detection Systems Using LSTM-RNN

Over the past few years, the challenge has been the increasing and significant attacks on anomaly detection processes. While attacks in anomaly detection can be easily predicted using Intrusion ...
TMCnet

Metal Detector Company Announces New Technology Development Platform

GARLAND, Texas, Jan. 20, 2026 /PRNewswire/ -- Texas-based Garrett Metal Detectors announced yesterday that its Paragon ™ walk-through metal detector is the first host for software engineered using ...
IEEE

Network Anomaly Detection for Intrusion Detection Systems Using Q-Learning and Deep Q-Learning: A Reinforcement Learning Approach

Abstract: Network anomaly detection is very important for making modern cybersecurity systems stronger, especially since cyber threats like zero-day attacks are becoming more complicated. This study ...
USA Today

The Apollo 500: AI-Driven Weapon Detection That Improves Safety And Efficiency

In high-traffic environments like hospitals, schools, and casinos, security leaders face an everyday trade-off: increased rigor with longer lines, frustrated visitors, and operational disruptions, or ...