Malware Classification with Word Embedding Features
This addresses malware classification for information security, but appears incremental as it combines existing embedding and classification methods.
The paper tackles malware classification by using opcode sequences to create feature vectors with HMM2Vec and Word2Vec embeddings, then applying various classifiers like SVM and CNN. It reports conducting extensive experiments across multiple malware families, claiming to extend beyond prior work.
Malware classification is an important and challenging problem in information security. Modern malware classification techniques rely on machine learning models that can be trained on features such as opcode sequences, API calls, and byte $n$-grams, among many others. In this research, we consider opcode features. We implement hybrid machine learning techniques, where we engineer feature vectors by training hidden Markov models -- a technique that we refer to as HMM2Vec -- and Word2Vec embeddings on these opcode sequences. The resulting HMM2Vec and Word2Vec embedding vectors are then used as features for classification algorithms. Specifically, we consider support vector machine (SVM), $k$-nearest neighbor ($k$-NN), random forest (RF), and convolutional neural network (CNN) classifiers. We conduct substantial experiments over a variety of malware families. Our experiments extend well beyond any previous work in this field.