Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Deep learning (DL) is one of the emerging and promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on the Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are presented. We also present feature extraction approaches and a review of recent DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on malware detection including future directions to further advance knowledge and research in this field.

翻译：恶意软件是当今最常见且危害最广泛的网络攻击形式之一。恶意软件可感染数百万台设备，实施包括窃取敏感数据、加密数据、瘫痪系统性能等在内的多种恶意活动。因此，恶意软件检测对于保护计算机和移动设备免受攻击至关重要。深度学习作为新兴且前景广阔的技术，为恶意软件检测提供了有效手段。针对桌面端和移动端平台不断涌现的恶意软件变种，深度学习算法凭借其处理大规模数据集的能力，成为构建可扩展且先进的恶意软件检测模型的有力工具。本文系统研究了当前用于检测Windows、Linux及Android平台恶意软件攻击的深度学习技术。具体而言，我们阐述了不同类别的深度学习算法、网络优化器及正则化方法，介绍了实现深度学习模型所使用的多种损失函数、激活函数和开发框架，梳理了特征提取方法，并综述了上述平台上基于深度学习进行恶意软件攻击检测的最新模型。此外，本文还指出了恶意软件检测领域的主要研究问题，包括未来可进一步深化该领域知识与研究的发展方向。