Deep anomaly detection of temporal heterogeneous data in AIOps: a survey

The advancement of the fifth generation (5G) mobile communication and Internet of Things (IoT) has facilitated the development of intelligent applications, but has also rendered these networks increasingly complex and vulnerable to various targeted attacks. Numerous anomaly detection (AD) models, particularly those using deep learning technologies, have been proposed to monitor and identify network anomalous events. However, the implementation of these models poses challenges for network operators due to lacking expert knowledge of these black-box systems. In this study, we present a comprehensive review of current AD models and methods in the field of communication networks. We categorize these models into four methodological groups based on their underlying principles and structures, with particular emphasis on the role of recent promising large language models (LLMs) in the field of AD. Additionally, we provide a detailed discussion of the models in the following four application areas: network traffic monitoring, networking system log analysis, cloud and edge service provisioning, and IoT security. Based on these application requirements, we examine the current challenges and offer insights into future research directions, including robustness, explainability, and the integration of LLMs for AD.