Anomaly Detection in Operating System Logs with Deep Learning-Based Sentiment Analysis

Hudan Studiawan*, Ferdous Sohel, Christian Payne

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

The purpose of sentiment analysis is to detect an opinion or polarity in text data. We can apply such an analysis to detect negative sentiment, which represents the anomalous activities in operating system (OS) logs. Existing methods involve manual searching, predefined rules, or traditional machine learning techniques to detect such suspicious events. In this article, we propose a novel deep learning-based sentiment analysis technique to check whether there are anomalous activities in OS logs. Log messages are modeled as sentences and we identify the sentiments using the gated recurrent unit (GRU) networks. OS log datasets inherently have a class imbalance in the sense that the number of negative sentiment is much lower than that of the number of positive ones. In order to address the class imbalance, we build a GRU layer on top of a class imbalance solver using the Tomek link method. Experimental results demonstrate that the proposed method can detect anomalous events in OS logs with an overall F1 and accuracy of 99.84 and 99.93 percent, respectively.

Original languageEnglish
Pages (from-to)2136-2148
Number of pages13
JournalIEEE Transactions on Dependable and Secure Computing
Volume18
Issue number5
DOIs
Publication statusPublished - Sept 2021

Keywords

  • Anomaly detection
  • class imbalance
  • deep learning
  • operating system logs
  • sentiment analysis

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