Cloud-based multiclass anomaly detection and categorization using ensemble learning

Faisal Shahzad, Abdul Mannan, Abdul Rehman Javed, Ahmad S. Almadhor, Thar Baker, Dhiya Al-Jumeily OBE

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number74
JournalJournal of Cloud Computing
Volume11
Issue number1
DOIs
Publication statusPublished - 3 Nov 2022

Keywords

  • Anomaly detection
  • Cloud computing
  • Cyberattacks
  • Deep learning
  • Ensemble learning
  • Multiclass attack

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