Citation

Javeed, Mohammed Saad and Maua, Jannatul and Islam, Rahomotul and Ahmed, Mumtahina and Mridha, M. F. and Hossen, Md. Jakir (2026) A Multi-Task Neural Framework for Unified Alert Processing and Incident Prediction in Enterprise IT Systems. IEEE Open Journal of the Computer Society, 7. pp. 264-275. ISSN 2644-1268

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Abstract

Effective incident management in modern IT systems requires timely interpretation and routing of alerts generated from diverse sources such as SNMP Traps, Syslog messages, and xMatters notifications. However, conventional frameworks often lack unified processing and intelligent automation, resulting in delayed response and SLA violations. This paper presents an AI-enhanced unified alerting and incident managementframeworkthat integrates heterogeneous alert streams via the ServiceNow platform. Leveraging two real-world datasets comprising over 140,000 event records and 24,000 unique incidents, we implement a multi-task deep neural network to jointly predict resolution time, incident priority, and responsible as signment group. The proposed method incorporates temporal feature engineering, trainable embeddings for categorical data, and variational autoencoders for dimensionality reduction. A synthetic alert-source simulation is introduced to mimic real-world alert diversity within the data pipeline. Experimental results demonstrate superior performance over baseline models in all key metrics, validating the effectiveness of the proposed architecture. The framework sets the stage for scalable, automated, and context-aware incident triaging in enterprise IT environments.

Item Type:	Article
Uncontrolled Keywords:	Incident management, alert correlation, multi-task learning, deep neural networks, ServiceNow, SNMP traps, syslog, xMatters, resolution time prediction, IT operations analytics
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	09 Feb 2026 04:05
Last Modified:	09 Feb 2026 04:05
URII:	http://shdl.mmu.edu.my/id/eprint/15233

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