5 papers
TRIS: A Tri-Layer Retrieval Integrity Sieve Against Knowledge Poisoning
Muhaimin Bin Munir, Akib Jawad Ononto, Nazia Shehnaz Joynab +2
Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical attack surface: PoisonedRAG sho…
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…
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…