6 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…
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…
NOMAD -- Navigating Optimal Model Application to Datastreams
Ashwin Gerard Colaco, Sharad Mehrotra, Michael J De Lucia +5
NOMAD (Navigating Optimal Model Application for Datastreams) is an intelligent framework for data enrichment during ingestion that optimizes realtime multiclass classification by d…
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
Khandakar Ashrafi Akbar, Md Nahiyan Uddin, Latifur Khan +4
As connected and automated transportation systems evolve, there is a growing need for federal and state authorities to revise existing laws and develop new statutes to address emer…
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…