This project implements a comprehensive, end-to-end machine learning pipeline for detecting network ... binary anomaly detection (Benign vs. Attack) and multi-class classification (identifying ...

This project is an Intrusion Detection System (IDS) using machine learning (ML) and deep learning (DL) to detect network intrusions. It leverages the CICIDS2018 dataset to classify traffic as normal ...

As Internet of Things (IoT) devices proliferate in sectors like smart cities, health care, and industrial systems, they have ...

A recent study introduces an advanced anomaly-based intrusion detection system (IDS) designed to address the increasing cyber ...

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists ...

To explore LLMs' effectiveness in detecting network threats, researchers emulated a wireless communication environment using ...

Fraud is widespread in the United States and increasingly driven by technology. For example, 93% of credit card fraud now involves remote account access, not physical theft. In 2023, fraud losses ...