这篇论文用实际数据告诉你：用CWE分类训练HIDS能不能泛化到新CVE？结论是有的行有的不行，关键看行为轮廓而不是标签。
论文基于LID-DS-2021数据集，选取CWE-307（认证绕过）、CWE-89（SQL注入）、CWE-434（文件上传）三个CWE家族，提取66维Peng-Guo式特征向量，训练Isolation Forest和SGD One-Class SVM检测器。针对CWE-307，组合检测器在校准目标FPR=0.05时达到F1=0.6976（精确率0.8994，召回率0.5698），但CWE-89和CWE-434的F1均低于0.21。研究发现跨CVE迁移具有强烈方向依赖性，主要由源正常行为轮廓广度而非CWE标签决定。论文强调校准FPR是诚实报告的方法论前提。
From CVE to CWE: Syscall-Based HIDS Generalisation
Host intrusion detection systems (HIDS) based on system-call traces are typically trained and evaluated against individual Common Vulnerabilities and Exposures (CVE) instances. In operational settings, however, defenders need to recognise new exploits of an already known type of weakness. We empirically examine whether a one-class anomaly detector trained on the normal behaviour of a set of CVEs that share a Common Weakness Enumeration (CWE) class generalises to a different, unseen CVE inside the same class. Using six scenarios drawn from LID-DS-2021 and grouped into three CWE families (CWE-307 broken authentication, CWE-89 SQL injection, CWE-434 unrestricted file upload), we extract a 66-dimensional Peng-Guo-style feature vector per sliding window and train Isolation Forest and SGD One-Class SVM detectors with normal-only thresholds calibrated to fixed target false positive rates. We define and answer four research questions covering self-detection, asymmetric cross-CVE transfer, the value of a combined CWE-level normal profile, and the effect of feature filtering on transferability. The combined CWE-307 detector reaches F1 = 0.6976 at calibration target FPR = 0.05 (precision = 0.8994, recall = 0.5698), whereas CWE-89 and CWE-434 collapse to F1 <= 0.21 under the same protocol. Cross-CVE transfer turns out to be strongly direction-dependent and dominated by the breadth of the source normal profile rather than by the CWE label. We conclude that CWE-level generalisation in HIDS is empirically attainable for some but not all weakness families with current syscall features, and we argue that calibrated FPR is a methodological prerequisite for honest reporting in this setting.