New malware for HWP recordsdata continues to look due to the circumstances between South Korea and North Korea. There have been various studies to solve this drawback, however most of them are restricted as a end result of they require a large amount of effort to define options based mostly on skilled knowledge. In this examine, we designed a convolutional neural community to detect malware within HWP information. Our proposed model takes a uncooked byte stream as input and predicts whether it accommodates malicious actions or not. To incorporate extremely variable lengths of HWP byte streams, we propose a new padding technique and a spatial pyramid common pooling layer.

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