Microsoft

Undermining the trust boundary: Investigating a stealthy intrusion through third-party compromise

Microsoft Incident Response investigated an attack operated through legitimate and trusted administrative mechanisms to blend ...
IEEE

Intrusion Detection and Mitigation System for Smart Inverters Based on Machine Learning and Environmental Sensors

Abstract: The rapid expansion of renewable energy deployment has accelerated the adoption of smart inverters in solar farms. However, their reliance on communication networks introduces cybersecurity ...
Devdiscourse

IoT threat detection moves to transparent, decentralized AI systems

A new study published in Frontiers in Computer Science introduces a decentralized cybersecurity model that combines federated ...
IEEE

Machine learning-based intrusion detection and prevention using cross-layer features in Internet of Things (IoT) networks

Abstract: The IoT has emerged as a significant target for cyber-attacks, particularly with a focus on the routing protocol for low-power and lossy networks (RPL) within Wireless Sensor Networks (WSNs) ...
Scientific Research Publishing

Xu, T., et al. (2024) Reinforcement Learning for Adaptive Intrusion Detection: Balancing Performance and False Positives. IEEE Transactions on Neural Networks and Learning ...

ABSTRACT: Cybersecurity has emerged as a global concern, amplified by the rapid expansion of IoT devices and the growing digitization of systems. In this context, traditional security solutions such ...
PNAS

In silico machine learning–enabled detection of polycyclic aromatic hydrocarbons from contaminated soil

Soil contamination by environmental pollutants, particularly polycyclic aromatic hydrocarbons (PAHs), can significantly affect human health due to their carcinogenic and mutagenic properties. In this ...