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Found 6 papers, 2 papers with code
Profiler: Profile-Based Model to Detect Phishing Emails
The Profiler does not require large data sets to train on to be effective and its analysis of varied email features reduces the impact of concept drift.
Towards Web Phishing Detection Limitations and Mitigation
However, with an average of 10K phishing links reported per hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of such ML-based solutions are laid bare.
Characterizing Malicious URL Campaigns
Seemingly dissimilar URLs are being used in an organized way to perform phishing attacks and distribute malware.
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.
