4 citations · 8 across the 12 of their papers we have counts for
12 papers
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
Alicia Russell-Gilbert, Sudip Mittal, Shahram Rahimi +4
Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictiv…
Multivariate Data Augmentation for Predictive Maintenance using Diffusion
Andrew Thompson, Alexander Sommers, Alicia Russell-Gilbert +7
Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and pre…
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
Alicia Russell-Gilbert, Alexander Sommers, Andrew Thompson +7
For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore…
Explainable Anomaly Detection: Counterfactual driven What-If Analysis
Logan Cummins, Alexander Sommers, Sudip Mittal +4
There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly det…
A Survey of Transformer Enabled Time Series Synthesis
Alexander Sommers, Logan Cummins, Sudip Mittal +4
Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art. Application of these m…
Generating Synthetic Time Series Data for Cyber-Physical Systems
Alexander Sommers, Somayeh Bakhtiari Ramezani, Logan Cummins +4
Data augmentation is an important facilitator of deep learning applications in the time series domain. A gap is identified in the literature, demonstrating sparse exploration of th…
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems
Jesse Ables, Nathaniel Childers, William Anderson +4
This paper addresses trust issues created from the ubiquity of black box algorithms and surrogate explainers in Explainable Intrusion Detection Systems (X-IDS). While Explainable A…
Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
Logan Cummins, Alex Sommers, Somayeh Bakhtiari Ramezani +4
Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to pred…
Explainable Intrusion Detection Systems Using Competitive Learning Techniques
Jesse Ables, Thomas Kirby, Sudip Mittal +4
The current state of the art systems in Artificial Intelligence (AI) enabled intrusion detection use a variety of black box methods. These black box methods are generally trained u…
Designing an Artificial Immune System inspired Intrusion Detection System
William Anderson, Kaneesha Moore, Jesse Ables +4
The Human Immune System (HIS) works to protect a body from infection, illness, and disease. This system can inspire cybersecurity professionals to design an Artificial Immune Syste…
Creating an Explainable Intrusion Detection System Using Self Organizing Maps
Jesse Ables, Thomas Kirby, William Anderson +4
Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS) are complex black boxes. This means that a security analyst will have little to no explanation or clar…
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
Subash Neupane, Jesse Ables, William Anderson +4
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespr…