Isolation Forest-Based Anomaly Detection for Cloud-Native Banking IT Infrastructure: A Simulation-Oriented Study - Standalone study derived from the Isolation Forest component of the supplied main research paper

Cloud-native banking platforms generate heterogeneous operational telemetry from applications, infrastructure components, distributed services, and observability pipelines. Detecting unusual behavior in such environments is difficult because operational anomalies may be sparse, diverse, and insufficiently labeled for conventional supervised learning. This paper isolates the Isolation Fore st component described in a larger study of self-adaptive banking IT infrastructure and develops it as a standalone anomaly-detection research study. The proposed approach uses Isolation Forest as an unsupervised detector over incoming telemetry, where observations that can be separated from the majority with relatively short random-partition paths receive higher anomaly scores. The source study describes a controlled synthetic environment representing 30 days of banking activity across five service clusters and containing application and infrastructure logs, time-series metrics, distributed traces, injected faults, and simulated attack scenarios. That environment is used here as the experimental context, while the analysis deliberately excludes the source study's LSTM Autoencoder and other agentic components. A critical attribution issue is identified: the source paper reports its principal anomaly-detection metrics for a hybrid Isolation Forest plus LSTM Autoencoder pipeline. Consequently, its reported 96.3% precision, 94.8% recall, 95.5% Fl-score, 97.l% accuracy, 2.7% false- positive rate, and 0.98 AUC-ROC cannot legitimately be presented as Isolation Forest-only results. The paper therefore provides a reproducible evaluation framework for standalone Isolation Forest and treats the hybrid measurements only as contextual evidence. The contribution is a focused methodology, an explicit validity boundary, and a simulation-oriented protocol for determining the independent value of Isolation Forest in banking IT anomaly detection.