COMPARATIVE ANALYSIS OF SEQUENCE-BASED AND GRAPH-BASED DEEP LEARNING FOR ANOMALY DETECTION IN MICROSERVICE TRACES

Yesset Zhussupov; Ainur Zhumadillayeva; Shona Shinassylov; Ainur Amirtayeva

doi:10.26577/jpcsit4220265

Authors

Yesset Zhussupov L.N. Gumilyov Eurasian national university, Astana, Kazakhstan https://orcid.org/0009-0001-0946-8094 (unauthenticated)
Ainur Zhumadillayeva L.N. Gumilyov Eurasian national university, Astana, Kazakhstan https://orcid.org/0000-0003-1042-0415 (unauthenticated)
Shona Shinassylov Al-Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0009-0002-5491-7255 (unauthenticated)
Ainur Amirtayeva Al-Farabi Kazakh National University, Almaty, Kazakhstan https://orcid.org/0009-0008-4579-287X (unauthenticated)

DOI:

https://doi.org/10.26577/jpcsit4220265

Keywords:

microservice architecture, anomaly detection, graph neural networks, LSTM, distributed tracing, deep learning, observability

Abstract

The transition from monolithic to microservice architectures has significantly increased the complexity of system observability. Distributed tracing has emerged as a vital tool for monitoring inter-service interactions, yet traditional anomaly detection methods often fail to capture the intricate structural relationships inherent in these systems. This article presents a comprehensive comparative analysis of sequence-based machine learning approaches, specifically Long Short-Term Memory (LSTM) networks, with Graph-based Deep Learning methods such as Graph Neural Networks (GNNs) for anomaly detection in microservice traces. We evaluate these paradigms across three critical dimensions: structural awareness, detection accuracy, and computational efficiency. Our analysis, drawn from recent frameworks including DeepTraLog and TraceVAE, demonstrates that graph-based models provide superior structural fidelity and achieve higher precision-recall metrics while maintaining scalability for high-dimensional telemetry data. The findings indicate that GNN-based approaches achieve F1-scores up to 0.954, significantly outperforming sequence-based alternatives which typically achieve F1-scores around 0.741. These results have important implications for the design of next-generation observability platforms for cloud-native applications.

Downloads

Download data is not yet available.

Author Biographies

Yesset Zhussupov , L.N. Gumilyov Eurasian national university, Astana, Kazakhstan

Yesset Zhussupov is a PhD student in Software Engineering at L.N. Gumilyov Eurasian National University (Almaty, Kazakhstan; yesset.zhussupov@gmail.com). He received his MSc in Software Engineering from the same university in 2022. Mr. Zhussupov serves as Chief Technology Officer at an AFSA-licensed brokerage organization, where he architects systems integrating big data and machine learning technologies. His research focuses on neural network applications in finance and economics, with particular emphasis on market forecasting and predictive modeling. His recent work includes the paper "Study of the Possibilities of Using Deep Artificial Intelligence in Forecasting the Green Paper Market in Kazakhstan." In 2015, he received the Intel Excellence Award for his work applying neural networks to computer vision–based emotion recognition.
Ainur Zhumadillayeva, L.N. Gumilyov Eurasian national university, Astana, Kazakhstan

PhD Dr. Ainur Zhumadillayeva is an Associate Professor of Associate Professor of Computer and Software Engineering at L. N. Gumilyov Eurasian National University (Astana, Kazakhstan). She received her PhD degree in the specialty *05.13.18 – Mathematical Modeling, Numerical Methods, and Software Packages* from the Institute of Mathematics of the Ministry of Education and Science of the Republic of Kazakhstan (Almaty) in 2010. Ainur Zhumadillayeva is the author and coauthor of educational publications and scientific monographs and has published more than 60 scientific and educational-methodical works in Kazakhstan and abroad, including publications indexed in international databases such as Scopus and Clarivate Analytics. Her research interests include mathematical modeling, numerical methods, machine learning, and artificial neural networks. She is a Member of the Institute of Electrical and Electronics Engineers (IEEE).
Shona Shinassylov, Al-Farabi Kazakh National University, Almaty, Kazakhstan

Shona Shinassylov is a Researcher and Lecturer at the Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty, Kazakhstan. He received his Master's degree in Information Technology from Al-Farabi Kazakh National University and completed his doctoral studies in 2025. His research interests include digital twins, artificial intelligence, machine learning, deep reinforcement learning, smart buildings, the Internet of Things (IoT), energy management systems, and intelligent decision-support systems. His current research focuses on the design and development of virtual prototypes of smart building energy management systems based on digital twin technologies, machine learning methods, and multi-objective reinforcement learning.
Ainur Amirtayeva, Al-Farabi Kazakh National University, Almaty, Kazakhstan

Ainur Amirtayeva is a Master's student in the dual-degree program between Al-Farabi Kazakh National University (KazNU) and Northwestern Polytechnical University (NWPU) (Almaty, Kazakhstan; amirtayevaainur@gmail.com). She received her Bachelor's degree in Information Security Systems from Al-Farabi Kazakh National University. Her academic and professional interests include cybersecurity, artificial intelligence, machine learning, deep learning, neural networks, parallel and distributed programming, phishing detection, computer vision, network traffic analysis, intrusion detection systems, and the application of AI in healthcare. Her research focuses on the development of intelligent information security systems, the application of neural networks, and the use of parallel computing techniques to improve machine learning algorithms.