Wee Ki Joon Publishes Article on Using CNNs and Entropy-Based Feature Selection for Malware Detection

Wee Ki Joon, an intern at the ISC as part of the SANS.edu Applied Cybersecurity Bachelor's Degree Program (BACS), published an article on March 26 about using Convolutional Neural Networks (CNNs) and entropy-based feature selection to identify potential malware artifacts. The article details an advanced method for detecting suspicious elements using Convolutional Neural Networks (CNNs) and entropy-based feature selection techniques. This approach aims to improve the efficiency of threat detection in cybersecurity.