Sequential Embedding-based Attentive (SEA) classifier for malware classification

TL;DR

This paper introduces a lightweight malware classifier using NLP techniques, achieving high accuracy and low log loss, suitable for diverse devices with varying resource capabilities.

Contribution

The paper proposes a novel Sequential Embedding-based Attentive (SEA) classifier that effectively detects malware across heterogeneous devices, combining NLP methods with a lightweight design.

Findings

Achieved 99.13% accuracy on benchmark dataset

Attained a log loss score of 0.04

Demonstrated effectiveness on resource-constrained devices

Abstract

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.