25 citations · 77 across the 18 of their papers we have counts for
8 papers · 1 filter
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…
PEAR: Planner-Executor Agent Robustness Benchmark
Shen Dong, Mingxuan Zhang, Pengfei He +4
Large Language Model (LLM)-based Multi-Agent Systems (MAS) have emerged as a powerful paradigm for tackling complex, multi-step tasks across diverse domains. However, despite their…
MUBox: A Critical Evaluation Framework of Deep Machine Unlearning
Xiang Li, Bhavani Thuraisingham, Wenqi Wei
Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution…
Multi-concept adversarial attacks
Vibha Belavadi, Yan Zhou, Murat Kantarcioglu +1
As machine learning (ML) techniques are being increasingly used in many applications, their vulnerability to adversarial attacks becomes well-known. Test time attacks, usually laun…
Fairness-Aware Online Meta-learning
Chen Zhao, Feng Chen, Bhavani Thuraisingham
In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta Learning (OML) learns good priors over model parameters (or learnin…
Imbalanced Adversarial Training with Reweighting
Wentao Wang, Han Xu, Xiaorui Liu +3
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks. However, almost all existing studies abou…