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Cloud-based multiclass anomaly detection and categorization using ensemble learning

Shahzad, F, Mannan, A, Javed, AR, Almadhor, AS, Baker, T and Al-Jumeily OBE, D (2022) Cloud-based multiclass anomaly detection and categorization using ensemble learning. Journal of Cloud Computing, 11 (1).

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Open Access URL: https://doi.org/10.1186/s13677-022-00329-y (Published version)


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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: Springer
SWORD Depositor: A Symplectic
Date Deposited: 15 Dec 2022 10:10
Last Modified: 15 Dec 2022 10:15
DOI or ID number: 10.1186/s13677-022-00329-y
URI: https://researchonline.ljmu.ac.uk/id/eprint/18406
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