A recent study introduces an advanced anomaly-based intrusion detection system (IDS) designed to address the increasing cyber threats targeting Internet of Things (IoT) devices. By combining machine learning (ML) and deep learning (DL) models with particle swarm optimization (PSO) for feature selection, the system achieves remarkable performance. Tested on the RT_IoT2022 dataset, the system's top performer, CatBoost, achieved an impressive 99.85% accuracy, making it one of the most accurate IDS solutions for IoT environments. This innovation offers a powerful tool to detect and classify complex attacks in real-time, enhancing cybersecurity for resource-constrained IoT networks.

Tackling high-grade neuroendocrine tumors, unlocking the mysteries of kidney cancer metabolism, making comprehensive cancer services more accessible, emerging trends after an FDA committee ruling favors using a new endpoint in multiple myeloma trials, and more are in this month’s tip sheet from Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine.

Innovations from the U of A College of Medicine – Tucson, the Asthma and Airway Disease Research Center and the BIO5 Institute are targeting the deeper causes of asthma and COPD.