发表机构
School of Computer Science and Engineering, University of Sunderland（桑德兰大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究恶意软件表示在特征空间扰动下的潜在稳定性，提出潜在稳定性分析管道，比较多种表示方法，定义LED等指标，通过实验得出不同表示在干净分类及扰动下的表现，所提方法虽未超基线，但有额外诊断价值。
AI 中文摘要
静态恶意软件检测器通常使用诸如准确率、F1、ROC AUC和PR AUC等干净样本指标进行评估。然而，这些指标对于学习到的恶意软件表示在特征向量受到扰动时的表现、样本向不确定决策区域移动的程度，或压缩表示是否保留安全相关结构的洞察有限。本文提出了一种用于EMBER特征空间中恶意软件扰动评估的潜在稳定性分析管道。该管道比较了完整的EMBER特征、基于PCA的压缩、beta/去噪变分自编码器表示、受曼德布洛特启发的逃逸时间描述符和PINN风格的潜在流模块。我们定义了潜在逃逸散度（LED）来测量扰动下逃逸时间分布的变化，并使用PINNFlow衍生的残差、速度、风险和梯度偏移指标来表征潜在运动。实验使用180,000个训练样本、180,000个测试样本和240,000个验证样本在EMBER静态PE特征向量上进行。完整的EMBER特征在干净分类性能方面最强，ROC AUC为0.9962，F1为0.9713，而PCA - 64是最强的压缩基线，ROC AUC为0.9846，F1为0.9347。所提出的VAE + 曼德布洛特 + PINNFlow表示在干净分类方面没有超过这些基线，但在受控的特征空间扰动探测下提供了额外的诊断价值。
英文摘要
Static malware detectors are commonly evaluated using clean-sample metrics such as accuracy, F1, ROC AUC, and PR AUC. However, these metrics provide limited insight into how learned malware representations behave when feature vectors are perturbed, how close samples move toward uncertain decision regions, or whether compressed representations preserve security-relevant structure. This paper presents a latent-stability analysis pipeline for malware perturbation assessment in EMBER feature space. The pipeline compares full EMBER features, PCA-based compression, beta/denoising variational autoencoder representations, Mandelbrot-inspired escape-time descriptors, and a PINN-style latent-flow module. We define Latent Escape Divergence (LED) to measure changes in escape-time profiles under perturbation, and use PINNFlow-derived residual, velocity, risk, and gradient-shift metrics to characterize latent movement. Experiments are conducted on EMBER static PE feature vectors using 180,000 training samples, 180,000 test samples, and 240,000 holdout samples. Full EMBER features achieve the strongest clean classification performance with ROC AUC of 0.9962 and F1 of 0.9713, while PCA-64 is the strongest compressed baseline with ROC AUC of 0.9846 and F1 of 0.9347. The proposed VAE+Mandelbrot+PINNFlow representation does not outperform these baselines for clean classification, but it provides additional diagnostic value under controlled feature-space perturbation probes.
