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Found 4 papers, 1 papers with code
Interpretability and Transparency-Driven Detection and Transformation of Textual Adversarial Examples (IT-DT)
It focuses on interpretability and transparency in detecting and transforming textual adversarial examples.
ReinforceBug: A Framework to Generate Adversarial Textual Examples
Adversarial Examples (AEs) generated by perturbing original training examples are useful in improving the robustness of Deep Learning (DL) based models.
Machine Learning for Detecting Data Exfiltration: A Review
Objective: This paper aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures.
Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors
Therefore, the security vulnerabilities of these systems, in general, remain primarily unknown which calls for testing the robustness of these systems.
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