用深度强化学习提升恶意软件溯源准确率，从7%升至98%
Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning
- 基于深度强化学习构建恶意软件溯源模型
- 准确率从7%提升至98%，显著优于早期版本
- 适合安全研究者与威胁情报分析人员参考
为开发用于恶意软件溯源的深度强化学习（DRL）模型，进行了大量研究、迭代编码及基于前代模型和最新论文的调整。初期模型准确率仅约7%，通过持续优化架构与学习算法，准确率在早期迭代中大幅提升至73%以上。训练后期，模型准确率稳定接近98%，展现出对恶意软件活动的精准识别与归属能力。图示清晰展示了模型训练过程中准确率的上升趋势，反映了其随时间不断成熟和性能提升的过程。
原文摘要 · Abstract (English)
The development of the DRL model for malware attribution involved extensive research, iterative coding, and numerous adjustments based on the insights gathered from predecessor models and contemporary research papers. This preparatory work was essential to establish a robust foundation for the model, ensuring it could adapt and respond effectively to the dynamic nature of malware threats. Initially, the model struggled with low accuracy levels, but through persistent adjustments to its architecture and learning algorithms, accuracy improved dramatically from about 7 percent to over 73 percent in early iterations. By the end of the training, the model consistently reached accuracy levels near 98 percent, demonstrating its strong capability to accurately recognise and attribute malware activities. This upward trajectory in training accuracy is graphically represented in the Figure, which vividly illustrates the model maturation and increasing proficiency over time.
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