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Cloud-based multiclass anomaly detection and categorization using ensemble learning
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DOI：10.1186/s13677-022-00329-y.png)
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Abstract
En 中文
The world of the Internet and networking is exposed to many cyber-attacks and threats. Over the years, machine learning models have progressed to be integrated into many scenarios to detect anomalies accurately. This paper proposes a novel approach named cloud-based anomaly detection (CAD) to detect cloud-based anomalies. CAD consist of two key blocks: ensemble machine learning (EML) model for binary anomaly classification and convolutional neural network long short-term memory (CNN-LSTM) for multiclass anomaly categorization. CAD is evaluated on a complex UNSW dataset to analyze the performance of binary anomaly detection and categorization of multiclass anomalies. Furthermore, the comparison of CAD with other machine learning conventional models and state-of-the-art studies have been presented. Experimental analysis shows that CAD outperforms other studies by achieving the highest accuracy of 97.06% for binary anomaly detection and 99.91% for multiclass anomaly detection.
Keywords:
Cloud computing
Anomaly detection
Cyberattacks
Deep learning
Ensemble learning
Multiclass attack
Journal
J
IF:
4.3
Papers:
744
Citations:
2.2K
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