As cyber threats and malware attacks increasingly alarm both individuals and businesses, the urgency for proactive malware countermeasures intensifies. This has driven a rising interest in automated machine learning solutions. Transformers, a cutting-edge category of attention-based deep learning methods, have demonstrated remarkable success. In this paper, we present BERTroid, an innovative malware detection model built on the BERT architecture. Overall, BERTroid emerged as a promising solution for combating Android malware. Its ability to outperform state-of-the-art solutions demonstrates its potential as a proactive defense mechanism against malicious software attacks. Additionally, we evaluate BERTroid on multiple datasets to assess its performance across diverse scenarios. In the dynamic landscape of cybersecurity, our approach has demonstrated promising resilience against the rapid evolution of malware on Android systems. While the machine learning model captures broad patterns, we emphasize the role of manual validation for deeper comprehension and insight into these behaviors. This human intervention is critical for discerning intricate and context-specific behaviors, thereby validating and reinforcing the model's findings.

翻译：随着网络威胁和恶意软件攻击日益对个人和企业构成严重威胁，主动防御恶意软件的需求愈发迫切，这推动了自动化机器学习解决方案的兴起。Transformer作为一类基于注意力机制的尖端深度学习方法，已展现出显著成效。本文提出BERTroid——一种基于BERT架构的创新恶意软件检测模型。总体而言，BERTroid成为对抗安卓恶意软件的有前景解决方案，其性能超越现有先进方法的能力，彰显了其作为抵御恶意软件攻击的主动防御机制的潜力。此外，我们在多个数据集上对BERTroid进行评估，以检验其在不同场景下的性能。在动态发展的网络安全领域，我们的方法已展现出对安卓系统上快速演变的恶意软件的强大抗性。尽管机器学习模型能够捕捉宏观模式，但我们强调手动验证对于深入理解这些行为的关键作用。这种人工干预对于辨别复杂且依赖于上下文的具体行为至关重要，从而验证并强化模型的发现。