11 citations · 19 across the 3 of their papers we have counts for
3 papers
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Biagio Montaruli, Luca Demetrio, Maura Pintor +3
Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage. Nevertheless, the attacks recen…
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance
Savino Dambra, Yufei Han, Simone Aonzo +5
Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in di…
Longitudinal Study of the Prevalence of Malware Evasive Techniques
Lorenzo Maffia, Dario Nisi, Platon Kotzias +3
By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of…