Dynamic Anomaly Detection in Resource-Constrained Environments: Harnessing Robust Random Cut Forests for Resilient Cybernetic Defense

Investigating non-parametric anomalies and analyzing the influence of external factors on data integrity, uncovering hidden patterns amid dynamic fluctuations. This paper ex amines anomaly detection in resource-constrained environments using robust random cut forests. Begin with a detailed exploration of resilient random cut data structures for ana lyzing incoming data streams in Internet of Things environment. The methodology used in this paper is to evaluate using diverse datasets, including real-time Arduino data and publicly available sources, to assess algorithmic performance across different scenarios. This research contributes to the theoretical foundations of anomaly detection, stressing the need for adaptive approaches in evolving data landscapes. By employing robust random cut forests, the challenges posed by continuous data streams are addressed, en suring accurate identification of trends and anomalies over time. In summary, this study provides a comprehensive examination of anomaly detection in dynamic data streams, presenting a novel methodology grounded in resilient random partitioning forests. The integration of theoretical insights, updating strategies, and empirical experimentation lays the groundwork for future advancements in dynamic anomaly detection under re source constraints.