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

Muhammad Ahmed, Anam Qureshi, Jawwad Ahmed Shamsi, Murk Marvi
2/11/2023
cs.CRcs.LG

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy

84% match1 repo22025

A Natural Language Processing Approach to Malware Classification

81% match2023

A Survey of Malware Detection Using Deep Learning

80% match2024

Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research

80% match2024

Imbalanced malware classification: an approach based on dynamic classifier selection

80% match2025

Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid

79% match2024

Cite this paper

@article{ahmed2023sequential,
  title  = {Sequential Embedding-based Attentive (SEA) classifier for malware classification},
  author = {Muhammad Ahmed and Anam Qureshi and Jawwad Ahmed Shamsi and Murk Marvi},
  year   = {2023},
  eprint = {2302.05728},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.05728v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2302.05728.svg)](https://codeofpaper.com/paper/2302.05728)

Discussion