A survey of malware detection using deep learning

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.

恶意软件（恶意程序）检测和分类问题是一项复杂的任务，没有完美的方法。仍有大量工作要做。与大多数其他研究领域不同，恶意软件检测很难找到标准基准。本文旨在通过研究文本和图像分类中的深度学习（DL），以及使用预训练和多任务学习模型进行恶意软件检测的方法，以获得高精度，并探讨如果我们有一个标准基准数据集，哪种方法最佳，从而研究在MacOS、Windows、iOS、Android和Linux上使用深度学习进行恶意软件检测的最新进展。我们通过回顾这些深度学习分类器的有效性以及它们无法向深度学习开发者解释其决策和行为，讨论了使用深度学习分类器进行恶意软件检测的问题和挑战，提出了使用可解释机器学习（XAI）或可解释性机器学习（IML）程序的必要性。此外，我们讨论了对抗性攻击对深度学习模型的影响，这种攻击对其泛化能力产生负面影响，并导致在未见过的数据上性能不佳。我们认为有必要在不同的恶意软件数据集上训练和测试当前最先进的深度学习模型的有效性和效率。我们在各种数据集上研究了八种流行的深度学习方法。这项调查将有助于研究人员对使用深度学习进行恶意软件识别形成一般性理解。