BIOENGINEER

Kernel-Tuned Machine Learning Pushes Network Intrusion Detection Toward 99% Accuracy on Benchmark Data

A new study reports that Random Forest and Gaussian and Sigmoid kernel Support Vector Machines, combined with systematic ...
BIOENGINEER

New Feature Selection Method Trims Intrusion Detection Models for Industrial Edge Devices

A cluster-preserving feature selection framework cut intrusion detection features from 78 to 27 on the CICIDS2017 benchmark while Random Forest and XGBoost maintained roughly 99.9 percent accuracy.
GitHub

Intrusion and Vulnerability Detection in Software Defined Networks

Software-defined networks (SDNs) have significantly transformed the management and orchestration of modern networks, providing numerous advantages, including increased flexibility and programmability.
GitHub

literature-review.md

Physics-residual detection of CAN masquerade attacks on the ROAD dataset — physics-informed vs data-driven IDS attribution study - ihmorol/pinn-cps-robo ...