Dynamic Malware Analysis with Feature Engineering and Feature Learning
This work addresses malware detection for cybersecurity, offering a more efficient and accurate solution compared to existing methods that rely on complex feature engineering.
The paper tackles the problem of dynamic malware detection by proposing a novel feature extraction method using feature hashing and a deep neural network with Gated-CNNs and bidirectional LSTM, achieving significant performance improvements over baselines on a large real dataset.
Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released ("zero-day") malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.