A Natural Language Processing Approach to Malware Classification
This work addresses malware classification for cybersecurity applications, but it is incremental as it combines existing methods in a novel way.
The paper tackled malware classification by proposing a hybrid architecture using Hidden Markov Models (HMMs) on opcode sequences as feature vectors for classifiers, and found that an HMM-Random Forest model outperformed other techniques on a challenging dataset.
Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a form of feature engineering that is somewhat analogous to techniques that are commonly employed in Natural Language Processing (NLP). We find that this NLP-based approach outperforms other popular techniques on a challenging malware dataset, with an HMM-Random Forrest model yielding the best results.