Web Science and Digital Libraries Research Group

I am delighted to receive the Commonwealth Cyber Initiative Grant for $100,000 to support our collaborative proposal, “Adaptive Intrusion Detection in IoT Networks Using LLM-Driven Behavioral Analysis and Deep Reinforcement Learning” beginning in January 2025. This is a collaborative work with Dr. Neda Moghim and Virginia Tech.   Figure 1: Project Plan and Tasks This research project explores the integration of Deep Reinforcement Learning (DRL), Large Language Models (LLMs), neuro-symbolic AI, and wireless networking to create adaptive intrusion detection systems for Internet of Things (IoT) networks. The central research question focuses on developing resilient IoT systems capable of recovering swiftly from cyberattacks without degrading the user experience. To address this, the project introduces several key innovations. First, an adaptive prompt-generation system is proposed using DRL to optimize LLM queries in real-time by tracking the evolving nature of cy...

The Department of Computer Science (CS) at Old Dominion University (ODU) celebrated its sixth annual Trick-or-Research event on October 31, 2024, blending Halloween festivities with academic exploration. Check out our previous Trick-or-Research blog posts here: 2021 , 2022 , 2023 . This unique event brought together faculty, staff, and students to showcase the department's cutting-edge research and foster new collaborations. Designed especially to introduce undergraduate students to the vibrant research opportunities within the department, Trick-or-Research featured interactive lab tours, engaging demonstrations, and opportunities to network with professors and join research groups. Participants explored CS research labs in person at the E.V. Williams Engineering & Computational Sciences Building (E&CSB) and Dragas Hall or virtually via Gather.town . Adding to the festive spirit, students dressed up in creative Halloween costumes, ranging from Demon Slayer Rengoku Kyouj...

Image Generated by DALL-E The utility of Large Language Models (LLMs) across diverse tasks raises important questions about the role of AI in web archive contexts, particularly in facilitating change analysis tasks. For instance, can LLMs effectively detect and analyze changes in webpages? How can this be achieved? What advantages and limitations do AI approaches have compared to traditional methods?.  In this blog post, I summarize our paper  “Exploring Large Language Models for Analyzing Changes in Web Archive Content: A Retrieval-Augmented Generation Approach”  ( Jhon G. Botello ,  Lesley Frew ,  Jose J. Padilla ,  Michele C. Weigle ) published at the  9th Computational Archival Science (CAS) Workshop 2024  (check Lesley Frew's trip report for more details about the workshop). The paper represents our first step in determining the ability of LLMs and the extent to which they can assist with change analysis tasks. Motivation As we interact...

Figure 1. An Illustration of the transition from storytelling to advanced conceptualization. Image Generated by DALL-E. In my previous blog,— “ Do Large Language Models Agree on Entity Extraction? ”— I explored how different Large Language Models (LLMs) approach Named Entity Recognition (NER) tasks, highlighting the inconsistencies in entity identification and categorization. By leveraging techniques such as Sentence-BERT embeddings , and cosine similarity , I demonstrated how to unify outputs from multiple LLMs into a coherent and reliable list of entities, which is essential for tasks requiring consistent information extraction.  This blog transitions from entity extraction to a broader exploration of how LLMs move from narrative analysis to conceptual modeling and simulation . While extracting entities is a critical step in understanding narratives, the next challenge lies in structuring those narratives into meaningful models that can inform decision-making and support real...