2025· Proceedings of the 1st International Conference on Interdisciplinary Technology & Science Convergence (FusionX Global)· pp. 3-10· 0 citations· 17 references
TL;DR
The Mini-LM model is utilized to convert unstructured log messages from the Hadoop Distributed File System (HDFS) and Android datasets into 384-dimensional semantic embeddings, capturing contextual relationships.
Abstract
: Rapid growth in volume and complexity of system logs in modern computing environments requires efficient anomaly detection to identify potential system failures, security breaches, or performance issues. This paper introduces a novel approach to detecting anomalies in system logs using Small Language Models (SLMs). We utilize the Mini-LM model to convert unstructured log messages from the Hadoop Distributed File System (HDFS) and Android datasets into 384-dimensional semantic embeddings, capturing contextual relationships. These embeddings are combined with numerical features, including time differences and categorical encoded metadata, and processed by an Isolation Forest algorithm with a contamination rate of 0.2 to classify anomalies. A baseline model, using only process and thread identifiers, serves as a pseudo-ground truth for evaluation. The lightweight and modular pipeline ensures scalability and adaptability, making it suitable for real-time enterprise log analysis.
The quick advancement of smartphones and Android apps has created a high increase in malicious applications which potentially can steal sensitive information, track user activity, or cause damage to the equipment. Traditional Isolation Forest-based detectors rely on treating all the data as a single global distribution...
Yellamali Jaichand, K.Rani, Menapati Sruthi Raj et al.· International Workshop on Ar...· 0 citations
SeaLog is presented, an accurate, lightweight, and adaptive log-based anomaly detection framework that synergistically integrates the efficiency of traditional data-driven methods with the zero/few-shot learning ability of large language models (LLMs).
Jinyang Liu, Jun-Jie Huang, Zhihan Jiang et al.· ACM Transactions on Software...· 0 citations
Modern software systems generate large volumes of logs that record events, states, errors, and user or component activities. Manual inspection is increasingly unable to support timely fault diagnosis, performance monitoring, and security analysis. This review examines how machine learning is used to detect anomalies in...
Ji-Rong Han· Theoretical and Natural Scie...· 0 citations
System log anomaly detection (LogAD) requires balancing detection quality, interpretability, and deployment efficiency. While deep learning approaches achieve strong performance, they often rely on large labeled datasets and lack transparency. Recent large language model (LLM)-based methods improve reasoning but introd...
Cloud services require continuous log monitoring to detect failures before outages occur. Most log anomaly detectors rely on fixed event identifiers or templates, which degrade as services evolve, parsers change, and new templates appear, leading to a mismatch between offline accuracy and online reliability. We propose...
Detecting anomalies in system logs looks straightforward from the outside, yet logs are voluminous, the signalto-noise ratio is low, and standard Isolation Forest (IF) scores each entry in isolation, with no sense of whose entry it is; a user repeating one command in a loop stays invisible to it. This work addresses th...
Pathan Sardar Khan, Shaik Amanulla, Joseph Jayanth Vardhan Chimaladinne et al.· 2026 6th International Confe...· 0 citations
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