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Malware continues to evolve rapidly, and more than 450,000 new samples arecaptured every day, which makes manual malware analysis impractical. However,existing deep learning detection models need manual feature engineering orrequire high computational overhead for long training processes, which might belaborious to select feature space and difficult to retrain for mitigating modelaging. Therefore, a crucial requirement for a detector is to realize automaticand efficient detection. In this paper, we propose a lightweight malwaredetection model called SeqNet which could be trained at high speed with lowmemory required on the raw binaries. By avoiding contextual confusion andreducing semantic loss, SeqNet maintains the detection accuracy when reducingthe number of parameters to only 136K. We demonstrate the effectiveness of ourmethods and the low training cost requirement of SeqNet in our experiments.Besides, we make our datasets and codes public to stimulate further academicresearch.

SeqNet: An Efficient Neural Network for Automatic Malware Detection