Malware classification using deep learning

Abstract

We’ll display two different kinds of experiments, which are NIDS-based and Dynamic-based analysis shows how artificial intelligence (AI) helps us detecting and classify malware. On the NIDS-based intrusion detection, we use CICIDS2017 as a research dataset, embedding high dimensional features and find out redundant features in the raw dataset by Random Forest algorithm, reach 99.93% accuracy and 0.3% of the false alert rate. We extract the function calls in malware data by the method proposed in this paper to generate text data. The algorithm n-gram and TF-IDF are used to process text data, converts them into numeric features, and by another feature selection methods, we reduce the training time, achieve 87.08% accuracy, and save 87.97% training time in Dynamic-based analysis.