AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing

The abnormality of system behavior is inevitable in cloud computing because of its complexity and scale. How to perform anomaly detection on the system’s operating data to discover abnormal behavior has become a popular research field. However, anomaly detection is a challenging research problem bec...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computer communications Ročník 200; s. 86 - 94
Hlavní autoři: Jiang, Jun, Liu, Fagui, Ng, Wing W.Y., Tang, Quan, Zhong, Guoxiang, Tang, Xuhao, Wang, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.02.2023
Témata:
Anomaly detection
Cloud computing
Data stream
Ensemble learning
Imbalance
Cloud computing
Data stream
Imbalance
Ensemble learning
Anomaly detection
ISSN:0140-3664, 1873-703X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The abnormality of system behavior is inevitable in cloud computing because of its complexity and scale. How to perform anomaly detection on the system’s operating data to discover abnormal behavior has become a popular research field. However, anomaly detection is a challenging research problem because data in cloud computing scenarios is continuous, imbalanced, and cannot be acquired at once. In this work, an adaptive ensemble random fuzzy (AERF) algorithm is proposed for anomaly detection in cloud computing systems. The random fuzzy rule-based method in the AERF selects samples randomly to enhance the diversity of base classifiers for dealing with disturbances caused by abnormal sample distribution effectively. A dynamic weighting strategy is used for fuzzy classifier ensembles to improve processing efficiency and anomaly detection accuracy. Experimental results show that the AERF yields 10% higher AUC and G-mean than existing methods on SMD and EMOSCloud datasets and 20% higher AUC and G-mean on five benchmarking datasets. The AERF yields 20% higher F1 than existing methods on EMOSCloud dataset. For all datasets, the AERF yields 50% faster training time than other methods.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2023.01.004