Malware Detection, Enabled by Machine Learning

The document presents the performance metrics of various machine learning models in predicting malware and benign samples using a feature hashing correlation matrix. It reports accuracy, precision, recall, and mean absolute error (MAE) for models including linear support vector machine, multi-layer perceptron, Adaboost, and soft-voting method, with individual performance exceeding 95%. Additionally, it describes a Lua script for Suricata rules utilizing a machine learning model to analyze HTTP response bodies for potential malware detection.