期刊
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
卷 18, 期 5, 页码 2136-2148出版社
IEEE COMPUTER SOC
DOI: 10.1109/TDSC.2020.3037903
关键词
Sentiment analysis; Deep learning; Anomaly detection; Data models; Support vector machines; Operating systems; Social networking (online); Anomaly detection; sentiment analysis; deep learning; operating system logs; class imbalance
类别
资金
- Indonesia Lecturer Scholarship (BUDI) from Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia
This article introduces a deep learning-based sentiment analysis technique to detect anomalous activities in OS logs. By utilizing GRU networks and the Tomek link method, the class imbalance issue is successfully addressed, resulting in high accuracy and F1 scores.
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.
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