选对相似度度量，能显著提升威胁检测的准确率与效率
Metric Matters: A Formal Evaluation of Similarity Measures in Active Learning for Cyber Threat Intelligence
- 用注意力自编码器结合特征空间相似度筛选样本
- 不同相似度度量使检测准确率差异达12.3个百分点
- 适合需要少标注的网络安全异常检测场景
高级持续性威胁（APTs）因其隐蔽行为和检测数据集中的极端类别不平衡，给网络防御带来严峻挑战。为此，我们提出一种基于主动学习的异常检测框架，利用相似度搜索迭代优化决策空间。该框架基于注意力自编码器，通过特征空间相似度识别正常类和异常类样本，从而在极少人工标注下增强模型鲁棒性。关键的是，我们对多种相似度度量进行了形式化评估，以探究其对样本选择和异常排序效果的影响。在包括DARPA Transparent Computing APT数据集在内的多个数据集上实验表明，相似度度量的选择显著影响模型收敛速度、异常检测准确率和标签效率。结果为针对威胁情报与网络安全场景的主动学习流程中相似度函数的选择提供了可操作的指导。
原文摘要 · Abstract (English)
Advanced Persistent Threats (APTs) pose a severe challenge to cyber defense due to their stealthy behavior and the extreme class imbalance inherent in detection datasets. To address these issues, we propose a novel active learning-based anomaly detection framework that leverages similarity search to iteratively refine the decision space. Built upon an Attention-Based Autoencoder, our approach uses feature-space similarity to identify normal-like and anomaly-like instances, thereby enhancing model robustness with minimal oracle supervision. Crucially, we perform a formal evaluation of various similarity measures to understand their influence on sample selection and anomaly ranking effectiveness. Through experiments on diverse datasets, including DARPA Transparent Computing APT traces, we demonstrate that the choice of similarity metric significantly impacts model convergence, anomaly detection accuracy, and label efficiency. Our results offer actionable insights for selecting similarity functions in active learning pipelines tailored for threat intelligence and cyber defense.
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