A Survey of Malware Detection Using Deep Learning

• URL: http://arxiv.org/abs/2407.19153v1
• Date: Sat, 27 Jul 2024 02:49:55 GMT
• Title: A Survey of Malware Detection Using Deep Learning
• Authors: Ahmed Bensaoud, Jugal Kalita, Mahmoud Bensaoud,
• Abstract summary: This paper investigates advances in malware detection on Windows, iOS, Android, and Linux using deep learning (DL) We discuss the issues and the challenges in malware detection using DL classifiers. We examine eight popular DL approaches on various datasets.
• Score: 6.349503549199403
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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