Abstract
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.
Abstract (translated)
随着网络威胁和恶意软件攻击越来越多地警告个人和企业，对主动恶意软件应对措施的紧迫性加大了。这导致了对自动机器学习解决方案的浓厚兴趣。Transformer，一种关注式的深度学习方法，取得了显著的成功。在本文中，我们介绍了BERTroid，一种基于BERT架构的创新型恶意软件检测模型。总的来说，BERTroid成为对抗Android恶意软件的有前景的解决方案。其超越最先进的解决方案的能力表明了它在主动防御恶意软件攻击方面的潜力。此外，我们在多个数据集上评估BERTroid，以评估其在不同场景下的性能。在网络安全动态的背景下，我们的方法展示了对抗Android系统恶意软件快速演变的有前景的弹性。尽管机器学习模型捕捉到了广泛模式，但我们强调手动验证的重要性，以获得更深入和全面的了解这些行为。这种人类干预对于区分复杂和上下文特异性行为至关重要，从而验证和加强 model 的研究结果。
URL
https://arxiv.org/abs/2405.03620
