Author(s)

Amelia Ford

Affiliation(s)

School of Computing, University of Otago, Dunedin, New Zealand.

Corresponding Author

Amelia Ford

ABSTRACT

The increasing complexity and scale of microservice-based architectures have introduced new challenges in monitoring and anomaly detection. Traditional supervised learning methods often require extensive labeled data, which is impractical in dynamic and evolving environments. This paper presents an unsupervised anomaly detection framework based on autoencoders and temporal pattern modeling to identify abnormal behavior in microservice systems. By learning the reconstruction error of multivariate time-series data collected from microservice performance metrics, the model effectively distinguishes between normal and anomalous states. To capture temporal dependencies, we incorporate long short-term memory (LSTM) networks into the autoencoder architecture, enabling the detection of both point anomalies and contextual anomalies. Experimental evaluations on synthetic and real-world datasets demonstrate that our approach achieves high detection accuracy, low false positive rates, and robustness to unseen failure modes, making it suitable for real-time monitoring in production environments.

KEYWORDS

Microservices; Anomaly detection; Autoencoder; Unsupervised learning; Temporal patterns; LSTM; System monitoring; Root cause analysis

CITE THIS PAPER

Amelia Ford. Unsupervised anomaly detection in microservices using autoencoders and temporal patterns. Journal of Trends in Applied Science and Advanced Technologies. 2025, 2(1): 26-30. DOI: https://doi.org/10.61784/asat3013.

REFERENCES

[1] Oyeniran O C, Adewusi A O, Adeleke A G, et al. Microservices architecture in cloud-native applications: Design patterns and scalability. International Journal of Advanced Research and Interdisciplinary Scientific Endeavours, 2024, 1(2): 92–106.

[2] Abgaz Y, McCarren A, Elger P, et al. Decomposition of monolith applications into microservices architectures: A systematic review. IEEE Transactions on Software Engineering, 2023, 49(8): 4213–4242.

[3] Aksakalli I K, Celik T, Can A B, et al. Deployment and communication patterns in microservice architectures: A systematic literature review. Journal of Systems and Software, 2021, 180: 111014.

[4] Suleiman N, Murtaza Y. Scaling microservices for enterprise applications: Comprehensive strategies for achieving high availability, performance optimization, resilience, and seamless integration in large-scale distributed systems and complex cloud environments. Applied Research in Artificial Intelligence and Cloud Computing, 2024, 7(6): 46–82.

[5] Sheikh N. AI-Driven Observability: Enhancing System Reliability and Performance. Journal of Artificial Intelligence General Science (JAIGS), 2024, 7(1): 229–239.

[6] Aghazadeh Ardebili A, Hasidi O, et al. Enhancing resilience in complex energy systems through real-time anomaly detection: A systematic literature review. Energy Informatics, 2024, 7(1): 96.

[7] Podduturi S. AI for Microservice Monitoring & Anomaly Detection. International Journal of Emerging Trends in Computer Science and Information Technology, 2025: 192–211.

[8] Raeiszadeh M, Ebrahimzadeh A, Glitho R H, et al. Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications. IEEE Transactions on Machine Learning in Communications and Networking, 2025.

[9] Ramamoorthi V. Machine Learning Models for Anomaly Detection in Microservices. Quarterly Journal of Emerging Technologies and Innovations, 2020, 5(1): 41–56.

[10] Jeffrey N, Tan Q, Villar J R. A review of anomaly detection strategies to detect threats to cyber-physical systems. Electronics, 2023, 12(15): 3283.

[11] Xing S, Wang Y, Liu W. Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing. Sensors, 2025.

[12] Dixit A, Jain S. Contemporary approaches to analyze non-stationary time-series: Some solutions and challenges. Recent Advances in Computer Science and Communications, 2023, 16(2): 61–80.

[13] Sivaraman H. Adaptive Thresholding in ML-Driven Alerting Systems for Reducing False Positives in Production Environments, 2022.

[14] Sarker I H. Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2021, 2(3): 160.

[15] Nassif A B, Talib M A, Nasir Q, et al. Machine learning for anomaly detection: A systematic review. IEEE Access, 2021, 9: 78658–78700.

[16] Noshad Z, Javaid N, Saba T, et al. Fault detection in wireless sensor networks through the random forest classifier. Sensors, 2019, 19(7): 1568.

[17] e Oliveira E, Rodrigues M, Pereira J P, et al. Unlabeled learning algorithms and operations: Overview and future trends in defense sector. Artificial Intelligence Review, 2024, 57(3): 66.

[18] Gheibi O, Weyns D, Quin F. Applying machine learning in self-adaptive systems: A systematic literature review. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2021, 15(3): 1–37.

[19] Rashid U, Saleem M F, Rasool S, et al. Anomaly Detection using Clustering (K-Means with DBSCAN) and SMO. Journal of Computing & Biomedical Informatics, 2024, 7(2).

[20] Chalapathy R, Chawla S. Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407, 2019.

[21] Ahmed I, Ahmad M, Chehri A, et al. A smart-anomaly-detection system for industrial machines based on feature autoencoder and deep learning. Micromachines, 2023, 14(1): 154.

[22] Faseeha U, Syed HJ, Samad F, et al. Observability in Microservices: An in-depth exploration of frameworks, challenges, and deployment paradigms. IEEE Access, 2025.

[23] Wu B, Qiu S, Liu W. Addressing sensor data heterogeneity and sample imbalance: A transformer-based approach for battery degradation prediction in electric vehicles. Sensors, 2025, 25(11): 3564.

[24] Mienye ID, Swart TG, Obaido G. Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information, 2024, 15(9): 517.

[25] Chen S, Liu Y, Zhang Q, et al. Multi-distance spatial-temporal graph neural network for anomaly detection in blockchain transactions. Advanced Intelligent Systems, 2025: 2400898.

[26] Fang Z. Adaptive QoS‐Aware Cloud–Edge Collaborative Architecture for Real‐Time Smart Water Service Management, 2025.

[27] Vajda DL, Do TV, Bérczes T, et al. Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms. Scientific Reports, 2024, 14(1): 23288.

[28] Shao Z, Wang X, Ji E, et al. GNN-EADD: Graph neural network-based e-commerce anomaly detection via dual-stage learning. IEEE Access, 2025.

[29] Gortney ME, Harris PE, Cerny T, et al. Visualizing microservice architecture in the dynamic perspective: A systematic mapping study. IEEE Access, 2022, 10: 119999–120012.

[30] Li P, Ren S, Zhang Q, et al. Think4SCND: Reinforcement learning with thinking model for dynamic supply chain network design. IEEE Access, 2024.

[31] Wang J, Tan Y, Jiang B, et al. Dynamic marketing uplift modeling: A symmetry-preserving framework integrating causal forests with deep reinforcement learning for personalized intervention strategies. Symmetry, 2025, 17(4): 610.

[32] Johnson R. Designing secure and scalable IoT systems: Definitive reference for developers and engineers. HiTeX Press, 2025.