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Rasd: Semantic Shift Detection and Adaptation for Multi-Classification NIDS,
F. Alotaibi, S. Maffeis.
IFIPSEC 2024.
[PDF]
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Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation,
J. Thakkar, G. Zizzo, S. Maffeis.
PPAI@AAAI 2024.
[arXiv]
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Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience,
J. Thakkar, G. Zizzo, S. Maffeis.
DAI@AAAI 2024.
[arXiv]
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SQIRL: Grey-Box Detection of SQL Injection Vulnerabilities Using Reinforcement Learning,
S. Al Wahaibi, M. Foley, S. Maffeis.
USENIX Security 2023.
[PDF]
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Adaptive Experimental Design for Intrusion Data Collection,
K. Highnam, Z. Hanif, E. Van Vogt, S. Parbhoo, S. Maffeis, N. Jennings.
CAMLIS 2023.
[PDF]
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EarlyCrow: Detecting APT Malware Command and Control Over HTTP(S) Using Contextual Summaries,
A. Alageel, S. Maffeis.
ISC 2022.
[PDF]
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HAXSS: Hierarchical Reinforcement Learning for XSS Payload Generation,
M. Foley, S. Maffeis.
IEEE TrustCom 2022.
[PDF]
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VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detections,
H. Hanif, S. Maffeis.
IEEE IJCNN 2022.
[PDF]
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A Hybrid Graph Neural Network Approach for Detecting PHP Vulnerabilities,
R. Rabheru, H. Hanif, S. Maffeis.
IEEE DSC 2022.
[PDF]
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Certified Federated Adversarial Training,
G. Zizzo, A. Rawat, M. Sinn, S. Maffeis, C. Hankin.
NFFL@NeurIPS 2021.
[PDF]
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Hawk-Eye: Holistic Detection of APT Command and Control Domains,
A. Alageel, S. Maffeis.
ACM SAC 2021, (Security Track).
[PDF]