Multiple Severity-Level classifications for IT Incident Risk Prediction

Salman Ahmed, Muskaan Singh, Brendan Doherty, Effirul Ikhwan Ramlan, Kathryn Harkin, Damien Coyle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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The adoption of Artificial Intelligence (AI) is now widespread in Information Technology (IT) support. A particular area of interest is in the automation of IT incident management (i.e., the handling of an unusual event that hampers the quality of IT services in the most optimized manner). In this paper, we propose a framework using state-of-art algorithms to classify and predict the severity of such incidents (commonly labeled as High, Medium, and Low severity). We argue that the proposed framework would accelerate the process of handling IT incidents with improved accuracy. The experimentation was performed on the IT Service Management (ITSM) dataset containing 500,000 real-time incident descriptions with their encoded labels (Dataset 1) from a reputable IT firm. Our results showed that the Transformer models outperformed machine learning (ML) and other deep learning (DL) models with a 98% AUC score to predict the three severity classes. We tested our framework with an open-access dataset (Dataset 2) to further validate our findings. Our framework produced a 44% improvement in AUC score compared to the existing benchmark approaches. The results show the plausibility of AI algorithms in automating the prioritization of incident processing in large IT systems.
Original languageEnglish
Title of host publicationISCMI 2022 conference proceedings
Publication statusAccepted/In press - 2 Aug 2022

Bibliographical note

We are grateful for access to the Tier 2 High-Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant Nos. EP/T022175/ and EP/W03204X/1. Damien Coyle is supported by the UKRI Turing AI Fellowship 20212025 funded by the EPSRC (grant number EP/V025724/1). Salman Ahmed is supported by a Dr. George Moore Ph.D. scholarship. R


  • IT Incidents
  • Risk prediction
  • Dataset Imbalance
  • IT Service Management (ITSM)
  • Information Technology Infrastructure Library (ITIL)
  • Artificial Intelligence for IT Operations (AIOPS)


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