An Algorithmic Baseline for Temporal Telemetry Anomaly Detection in Enterprise Cloud Architectures
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I5P107Keywords:
Long Short-Term Memory (Lstm), Temporal Telemetry Analytics, Dynamic Ip Address Management (Ipam) Vulnerabilities, Sequential Cloud Security, Nist Sp 800-207 Zero Trust Architecture, Mean Absolute Error (Mae), Api-Driven Network TelemetryAbstract
Cloud architectures are increasingly complex, and an effective anomaly detection is needed to detect abnormal behaviours from a flux of network telemetry data that are constantly produced in enterprises. The concept of this paper is to detect the temporal telemetry anomalies with basic idea of algorithmic baseline with the sequential cloud security data analysis using Long Short-Term Memory (LSTM) network. The proposed framework aims to monitor temporal dynamics of API-driven network telemetry, look for changes in traffic behavior, and surface possible security anomalies in the context of API vulnerabilities in Dynamic IP Address Management (IPAM). The idea behind it is to leverage on existing telemetry sequences learning to know what is normal and utilizing Mean Absolute Error (MAE) as an indicator of prediction error and as an anomaly detection method. Moreover, its architecture designs follow the sequential principles of Cloud security – mapped to the Zero Trust Architecture (ZTA) principles as mapped by NIST SP 800-207 – Continuous monitoring and Contextual verification and adaptive threat visibility and verification. It is proposed to create a baseline containing sufficient information to model and analyse Telemetry and for security based anomaly analysis of any suspicious activity in the network across time both in a dynamic enterprise cloud environment. The aim of the project is to develop an algorithmic solution to contribute positive to the monitoring of cloud infrastructures, reducing the obscurity of anomalies and proactively assess cloud security actions for the changing, distributed and modular constructions.
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