Authors: Nisha Verma, Gaurav Nair, Swathi Reddy, Tarun Bhatia
Abstract: In modern IT ecosystems, syslogs serve as the primary diagnostic and auditing trail, capturing granular system-level, application, and security events. As infrastructures grow in scale and complexity spanning cloud-native applications, hybrid UNIX environments, and distributed edge deployments the volume of syslog data has become overwhelming. Traditional rule-based parsing methods and regex-driven filters struggle to scale across heterogeneous logs, leading to missed alerts, alert fatigue, and significant operational overhead. This review explores the transformative role of Natural Language Processing (NLP) and deep learning techniques in auto-categorizing syslogs with accuracy, adaptability, and semantic understanding. The paper begins with an overview of syslog formats, protocols, and the inherent variability in message content and structure. It then introduces modern NLP preprocessing techniques such as tokenization, entity masking, embedding strategies, and contextual vectorization. A detailed examination of deep learning architectures including CNNs, RNNs, LSTMs, and Transformer-based models like BERT is provided to demonstrate their effectiveness in capturing syntactic and contextual nuances. The review also presents methodologies for supervised, semi-supervised, and weakly supervised learning, with practical tools for building ground truth corpora. Operational pipeline considerations such as real-time streaming ingestion, model deployment, latency optimization, and SIEM integration are addressed. Use cases spanning data centers, telecom networks, and security monitoring highlight the practical impact of AI-based syslog categorization. Additionally, the article explores key challenges, including model interpretability, data privacy, false positives, and compliance risks. Future trends such as domain-specific Transformers, self-supervised log learning, federated training, and multi-modal observability are discussed as avenues for further innovation. Ultimately, this review positions NLP and AI as foundational to building scalable, intelligent, and proactive log management systems, paving the way for predictive operations and automated root cause analysis in complex enterprise environments.
DOI: https://doi.org/10.5281/zenodo.15846838
