  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.
