A Hybrid Cluster-Based Classification Model for Anomaly Detection in Unbalanced IoT Networks

Detecting anomalies in Internet of Things IoT networks is a critical security challenge, often hampered by highly imbalanced and diverse network traffic datasets. Standard classifiers struggle to perform well across all traffic types. This paper proposes a hybrid detection model to address this...

Lightweight Cluster-Based Federated Learning for Intrusion Detection in Heterogeneous IoT Networks

The rise of heterogeneous Internet of Things IoT devices has raised security concerns due to their vulnerability to cyberattacks. Intrusion Detection Systems IDS are crucial in addressing these threats. Federated Learning FL offers a privacy-preserving solution, but IoT heterogeneity and limited...

Researchers Propose New Steganography System for Hiding Data

A group of researchers has developed a new application that can hide sensitive data on a hard drive without encrypting it or leaving any obvious signs that the data is present. The new steganography system relies on the old principle of hiding valuables in plain sight. Developed by a group of...