Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

"Vcd2df" -- Leveraging Data Science Insights for Hardware Security Research

In this work, we hope to expand the universe of security practitioners of open-source hardware by creating a bridge from hardware design languages HDLs to data science languages like Python and R through novel libraries that convert VCD value change dump files into data frames, the expected input...