Timestamp Extraction and Log Classification Using Supervised Machine Learning: A Comparative Study

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]15/09/2025 at 12:21 PM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 26 38

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    Sayantika Laskar, Harsh Shukla,

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  1. Student, Student, Department of Computer Science and Engineering, Vellore Institute of Technology, Bhopal University, Bhopal, Department of Computer Science and Engineering, Vellore Institute of Technology, Bhopal University, Bhopal, Madhya Pradesh, Madhya Pradesh, India, India

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Abstract

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nIn modern software systems, logs are vital for monitoring application behavior, diagnosing issues, and analyzing performance. Timestamps are especially important for sequencing events, identifying anomalies, and understanding system failures. However, detecting timestamps in logs is challenging due to inconsistent formatting across systems and the presence of timestamp-like strings in non-timestamp fields. Traditional rule-based methods often fail in such cases. This study proposes a supervised machine learning approach to accurately classify timestamp tokens in structured log data. The methodology involves preprocessing log entries through tokenization and normalization, extracting relevant features, and generating a balanced dataset with timestamp and non-timestamp samples. Several classifiers, including Random Forest, K-Nearest Neighbors, Logistic Regression, XGBoost, and Support Vector Machines (SVM), were trained and evaluated. Performance was measured using accuracy, precision, recall, F1-score, and precision-recall curves. SVM achieved the best results across metrics and formats. The findings confirm that machine learning provides a scalable and reliable solution for automated timestamp detection in log analysis.nn

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Keywords: Timestamp detection, system logs, supervised learning, log analysis, machine learning, SVM, feature engineering, log parsing, anomaly detection, observability

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Software Engineering Tools & Technology Trends ]

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How to cite this article:
nSayantika Laskar, Harsh Shukla. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Timestamp Extraction and Log Classification Using Supervised Machine Learning: A Comparative Study[/if 2584]. Journal of Software Engineering Tools & Technology Trends. 15/09/2025; 12(03):26-38.

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How to cite this URL:
nSayantika Laskar, Harsh Shukla. [if 2584 equals=”][226 striphtml=1][else]Timestamp Extraction and Log Classification Using Supervised Machine Learning: A Comparative Study[/if 2584]. Journal of Software Engineering Tools & Technology Trends. 15/09/2025; 12(03):26-38. Available from: https://journals.stmjournals.com/josettt/article=15/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 20/06/2025
Accepted 23/07/2025
Published 15/09/2025
Retracted
Publication Time 87 Days

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