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.

翻译：智能设备的迅猛增长带来了多种安全威胁，其中最为突出的之一是恶意软件。恶意软件具有破坏设备乃至瘫痪整个网络系统的能力。因此，其早期检测与缓解对于避免灾难性后果至关重要。本研究提出了一种利用先进自然语言处理(NLP)技术的恶意软件检测方案。我们的核心目标是构建一个轻量级且高效的恶意软件分类器，使其能够适用于从资源受限设备到高性能计算设备等异构终端。该模型在基准数据集上进行测试，实现了99.13%的准确率和0.04的对数损失值。