2 citations · 2 across the 3 of their papers we have counts for
3 papers
Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
Yan Zhou, Kevin Hamlen, Michael De Lucia +5
Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traffic, yet deploying such mode…
SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
Umid Suleymanov, Murat Kantarcioglu, Kevin S Chan +6
Real-world systems must continuously adapt to novel concepts from limited data without forgetting previously acquired knowledge. While Few-Shot Class-Incremental Learning (FSCIL) i…
Controllable Fake Document Infilling for Cyber Deception
Yibo Hu, Yu Lin, Erick Skorupa Parolin +2
Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identi…