Real-Time Anomaly Detection with AI-Driven Syslog Monitoring for Bioinformatics Reliability

Sambasiva Rao  Madamanchi

doi:10.69968/ijisem.2025v4i2403-408

Authors

Sambasiva Rao Madamanchi Unix/Linux Administrator, Dept of Veterans Affairs (Austin, TX).

DOI:

https://doi.org/10.69968/ijisem.2025v4i2403-408

Keywords:

AI, Sys Log, Detection, Bioinformatics

Abstract

In this study, we present a novel framework that integrates AI-based anomaly detection with syslog monitoring to enhance the reliability of microbial pipelines. Syslog data, typically underutilized in bioinformatics, is collected and parsed to extract features such as error frequency, runtime patterns, and resource usage indicators. We apply machine learning models including Isolation Forests and LSTM Autoencoders to identify deviations from normal system behavior in real time. Experimental evaluations on both real and simulated microbial workflows demonstrate high accuracy in detecting anomalies, including those that do not trigger pipeline-level errors. A key use case illustrates how the system prevents corrupted outputs caused by unnoticed memory faults during taxonomic classification.

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