Journal article
Anomaly detection in operating system logs with deep Learning-based sentiment analysis
IEEE Transactions on Dependable and Secure Computing, Vol.18(5), pp.2136-2148
2020
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 work, 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%, respectively.
Details
- Title
- Anomaly detection in operating system logs with deep Learning-based sentiment analysis
- Authors/Creators
- H. Studiawan (Author/Creator) - Murdoch UniversityF. Sohel (Author/Creator) - Sepuluh Nopember Institute of TechnologyC. Payne (Author/Creator) - Sepuluh Nopember Institute of Technology
- Publication Details
- IEEE Transactions on Dependable and Secure Computing, Vol.18(5), pp.2136-2148
- Publisher
- IEEE
- Identifiers
- 991005541861407891
- Copyright
- © 2021 IEEE
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.47 Software Engineering
- 4.47.2804 Microservices Diagnostics
- Web Of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Information Systems
- Computer Science, Software Engineering
- ESI research areas
- Computer Science