  The tremendous growth in smart devices has uplifted several security threats.
One of the most prominent threats is malicious software also known as malware.
Malware has the capability of corrupting a device and collapsing an entire
network. Therefore, its early detection and mitigation are extremely important
to avoid catastrophic effects. In this work, we came up with a solution for
malware detection using state-of-the-art natural language processing (NLP)
techniques. Our main focus is to provide a lightweight yet effective classifier
for malware detection which can be used for heterogeneous devices, be it a
resource constraint device or a resourceful machine. Our proposed model is
tested on the benchmark data set with an accuracy and log loss score of 99.13
percent and 0.04 respectively.
