Computer Science / Machine Learning

A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers

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๐Ÿ“ Original Info

  • Title: A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers
  • ArXiv ID: 2512.23380
  • Date: 2025-12-29
  • Authors: Mohammad Nasirzadeh, Jafar Tahmoresnezhad, Parviz Rashidi-Khazaee

๐Ÿ“ Abstract

Log anomaly detection is crucial for preserving the security of operating systems. Depending on the source of log data collection, various information is recorded in logs that can be considered log modalities. In light of this intuition, unimodal methods often struggle by ignoring the different modalities of log data. Meanwhile, multimodal methods fail to handle the interactions between these modalities. Applying multimodal sentiment analysis to log anomaly detection, we propose CoLog, a framework that collaboratively encodes logs utilizing various modalities. CoLog utilizes collaborative transformers and multi-head impressed attention to learn interactions among several modalities, ensuring comprehensive anomaly detection. To handle the heterogeneity caused by these interactions, CoLog incorporates a modality adaptation layer, which adapts the representations from different log modalities. This methodology enables CoLog to learn nuanced patterns and dependencies within the data, enhancing its anomaly detection capabilities. Extensive experiments demonstrate CoLog's superiority over existing state-of-the-art methods. Furthermore, in detecting both point and collective...

๐Ÿ“„ Full Content

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