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Found 11 papers, 0 papers with code
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions.
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications.
Explainable Anomaly Detection: Counterfactual driven What-If Analysis
Inside of the field of explainable artificial intelligence, counterfactual explanations can give that information in the form of what changes to make to put the data point into the opposing class, in this case "healthy".
A Survey of Transformer Enabled Time Series Synthesis
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.
Generating Synthetic Time Series Data for Cyber-Physical Systems
Data augmentation is an important facilitator of deep learning applications in the time series domain.
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems
The contributions of this work include the hybrid X-IDS architecture, the eclectic rule extraction algorithm applicable to intrusion detection datasets, and a thorough analysis of performance and explainability, demonstrating the trade-offs involved in rule extraction speed and accuracy.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.
Explainable Intrusion Detection Systems Using Competitive Learning Techniques
Lastly, we analyze the statistical and visual explanations generated by our architecture, and we give a strategy that users could use to help navigate the set of explanations.
Designing an Artificial Immune System inspired Intrusion Detection System
The Human Immune System (HIS) works to protect a body from infection, illness, and disease.
Creating an Explainable Intrusion Detection System Using Self Organizing Maps
We leverage SOM's explainability to create both global and local explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
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 widespread malware attacks on critical systems such as cloud infrastructures and government institutions.
Explainable Artificial Intelligence (XAI)
Intrusion Detection
