Boundary-Seeking GAN-Augmented TabTransformer for Adversarially Robust Intrusion Detection

This collection summarizes nine recent security-research papers with practical defensive and offensive implications. Key contributions: (1) BGAN-augmented TabTransformer for flow-based intrusion detection (CICIDS2017) that synthetically balances classes and generates adversarial samples to both harden and test IDS models, substantially improving Macro-F1 and reducing adversarial performance degradation; (2) a surrogate-based, applicability-aware method for predicting remediation impact against opaque security-rating engines with a reliability layer to flag unstable predictions; (3) a design/実装