Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.00069 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914014245683200 |
|---|---|
| author | Balasubramanian, Prasasthy Kankanamge, Dumindu Gilman, Ekaterina Oussalah, Mourad |
| author_facet | Balasubramanian, Prasasthy Kankanamge, Dumindu Gilman, Ekaterina Oussalah, Mourad |
| contents | Conversational AI and Large Language Models (LLMs) have become powerful tools across domains, including cybersecurity, where they help detect threats early and improve response times. However, challenges such as false positives and complex model management still limit trust. Although Explainable AI (XAI) aims to make AI decisions more transparent, many security analysts remain uncertain about its usefulness. This study presents a framework that detects anomalies and provides high-quality explanations through visual tools BERTViz and Captum, combined with natural language reports based on attention outputs. This reduces manual effort and speeds up remediation. Our comparative analysis showed that RoBERTa offers high accuracy (99.6 %) and strong anomaly detection, outperforming Falcon-7B and DeBERTa, as well as exhibiting better flexibility than large-scale Mistral-7B on the HDFS dataset from LogHub. User feedback confirms the chatbot's ease of use and improved understanding of anomalies, demonstrating the ability of the developed framework to strengthen cybersecurity workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00069 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum Balasubramanian, Prasasthy Kankanamge, Dumindu Gilman, Ekaterina Oussalah, Mourad Machine Learning Conversational AI and Large Language Models (LLMs) have become powerful tools across domains, including cybersecurity, where they help detect threats early and improve response times. However, challenges such as false positives and complex model management still limit trust. Although Explainable AI (XAI) aims to make AI decisions more transparent, many security analysts remain uncertain about its usefulness. This study presents a framework that detects anomalies and provides high-quality explanations through visual tools BERTViz and Captum, combined with natural language reports based on attention outputs. This reduces manual effort and speeds up remediation. Our comparative analysis showed that RoBERTa offers high accuracy (99.6 %) and strong anomaly detection, outperforming Falcon-7B and DeBERTa, as well as exhibiting better flexibility than large-scale Mistral-7B on the HDFS dataset from LogHub. User feedback confirms the chatbot's ease of use and improved understanding of anomalies, demonstrating the ability of the developed framework to strengthen cybersecurity workflows. |
| title | AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.00069 |