mlsanityData to 7 Oct 2026

A Survey of Malware Detection Using Deep Learning

arXiv 2407.19153 ↗First posted 27 Jul 2024cs.CR cs.LGcited by 8

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Abstract (arXiv)

The problem of malicious software (malware) detection and classification is a complex task, and there is no perfect approach. There is still a lot of work to be done. Unlike most other research areas, standard benchmarks are difficult to find for malware detection. This paper aims to investigate recent advances in malware detection on MacOS, Windows, iOS, Android, and Linux using deep learning (DL) by investigating DL in text and image classification, the use of pre-trained and multi-task learning models for malware detection approaches to obtain high accuracy and which the best approach if we have a standard benchmark dataset. We discuss the issues and the challenges in malware detection using DL classifiers by reviewing the effectiveness of these DL classifiers and their inability to explain their decisions and actions to DL developers presenting the need to use Explainable Machine Learning (XAI) or Interpretable Machine Learning (IML) programs. Additionally, we discuss the impact of adversarial attacks on deep learning models, negatively affecting their generalization capabilities and resulting in poor performance on unseen data. We believe there is a need to train and test the effectiveness and efficiency of the current state-of-the-art deep learning models on different malware datasets. We examine eight popular DL approaches on various datasets. This survey will help researchers develop a general understanding of malware recognition using deep learning.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 33 of its 117 references that are in the map. First posted on arXiv 27 Jul 2024.

Builds on

33 of 117 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 3.

8 papers.

  1. 2024Gen-AI for User Safety: A Surveycited by 2
  2. 2024PBP: Post-training Backdoor Purification for Malware Classifierscited by 1
  3. 2025Optimized detection of cyber-attacks on IoT networks via hybrid deep learning modelscited by 1
  4. 2025Information Security Based on LLM Approaches: A Reviewcited by 1
  5. 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Modelscited by 1
  6. 2025A Novel Active Learning Approach to Label One Million Unknown Malware Variantscited by 0
  7. 2026Benchmarking Machine Learning Models for IoT Malware Detection under Data Scarcity and Driftcited by 0
  8. 2026ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classificationcited by 0