International Journal of Science, Engineering and TechnologyAuthors: Meena Kandasamy
Abstract: Linux server logs serve as a vital source of data, offering detailed insights into system activity, performance events, error reporting, and potential security breaches. As modern computing environments generate immense volumes of log data every second, manual inspection becomes impractical, leading to delays in detecting anomalies and responding to system faults or attacks. Anomaly detection in Linux server logs using Natural Language Processing (NLP) has emerged as a powerful technique to automate this analysis. NLP treats logs as unstructured textual data and employs linguistic and statistical techniques to uncover patterns, classify messages, and identify deviations that may indicate underlying issues. By applying tokenization, normalization, vectorization, and contextual embeddings, NLP models can extract meaningful patterns from log data, effectively transforming textual logs into structured data that machine learning algorithms can process. This article delves into the comprehensive pipeline required for NLP-driven anomaly detection in Linux logs, encompassing data preprocessing, feature engineering, model training, evaluation, and real-time deployment. It outlines the challenges posed by log heterogeneity, noise, and imbalance, and presents robust solutions such as transformer models, unsupervised learning, and ensemble detection techniques. Furthermore, it explores how NLP-based anomaly detection is being integrated into industry-grade tools, cloud environments, and continuous monitoring systems to support real-time incident detection and resolution. The discussion highlights the evolving role of explainability, security, and scalability in these models and suggests directions for future research, including federated learning and AIOps integration. This work aims to equip system administrators, data scientists, and cybersecurity professionals with a practical understanding of how to implement, optimize, and benefit from NLP-based anomaly detection in Linux server infrastructures
DOI: http://doi.org/10.5281/zenodo.16751746
