SecurityWeek

Cyber Insights 2026: Threat Hunting in an Age of Automation and AI

Understanding how threat hunting differs from reactive security provides a deeper understanding of the role, while hinting at how it will evolve in the future.
Tech Xplore

Detection of concealed explosives using terahertz spectral imaging and deep learning

Detecting concealed explosives and chemical threats constitutes a critical challenge in global security, yet current ...
IEEE

Enhanced Hybrid Intrusion Detection System using PSO-Optimized CNN Architecture

Abstract: Cyberattacks have grown into enduring threats, demanding advanced measures to secure vital data and systems. Although firewalls provide basic traffic filtering, they often fall short against ...
GitHub

RNN-DAS: A New Deep Learning Approach for Detection and Real-Time Monitoring of Volcano-Tectonic Events Using Distributed Acoustic Sensing

RNN-DAS is an innovative Deep Learning model based on Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells, developed for real-time Volcano-seismic Signal Recognition (VSR) using ...
IEEE

FSL-IDS: Federated Semi-Supervised Learning Intrusion Detection System for In-Vehicle Networks

Abstract: Intelligent in-vehicle networks (IVNs) are increasingly exposed to complex security threats. Traditional supervised deep learning methods depend heavily on extensive labeled datasets, ...