73 citations · 74 across the 5 of their papers we have counts for
6 papers
Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness
Mashal Zainab, Salijona Dyrmishi, Hamid Bostani +2
Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and at…
On the Effectiveness of Adversarial Training on Malware Classifiers
Hamid Bostani, Jacopo Cortellazzi, Daniel Arp +3
Adversarial Training (AT) is a key defense against Machine Learning evasion attacks, but its effectiveness for real-world malware detection remains poorly understood. This uncertai…
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
Hamid Bostani, Zhengyu Zhao, Veelasha Moonsamy
Machine learning (ML) has demonstrated significant advancements in Android malware detection (AMD); however, the resilience of ML against realistic evasion attacks remains a major…
Targeted and Troublesome: Tracking and Advertising on Children's Websites
Zahra Moti, Asuman Senol, Hamid Bostani +4
On the modern web, trackers and advertisers frequently construct and monetize users' detailed behavioral profiles without consent. Despite various studies on web tracking mechanism…
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection
Hamid Bostani, Zhengyu Zhao, Zhuoran Liu +1
Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD); however, ML models are vulnerable to realistic evasion attacks--crafting realizable Adver…
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
Hamid Bostani, Veelasha Moonsamy
Over the last decade, researchers have extensively explored the vulnerabilities of Android malware detectors to adversarial examples through the development of evasion attacks; how…