Anomaly Detection Using Maximum Entropy Fuzzy Clustering Algorithm Enhanced with Soft Computing Techniques

被引：0

作者：

Liang, Chunhua ^{[1
,2
]}

机构：

[1] Institute for History of Science and Technology, Shanxi University, Taiyuan

[2] Information Institute, Shanxi Finance and Taxation College, Taiyuan

来源：

Informatica (Slovenia) | 2024年 / 48卷 / 17期

关键词：

anomaly detection; fuzzy clustering; hilbert schmidt independence criterion; soft computing; unsupervised learning;

D O I：

10.31449/inf.v48i17.6537

中图分类号：

学科分类号：

摘要：

With the continuous growth of data volume, anomaly detection has become an important link in the data processing process. In view of the maximum entropy fuzzy clustering algorithm, an anomaly detection method combining soft computing is proposed. During the process, the K-means algorithm was used to construct the algorithm foundation, followed by the establishment of an objective function for maximum entropy calculation and the introduction of the Hilbert Schmidt independence criterion for variable extraction. Then it conducts data migration and calculates the exception score. The experimental results showed that the proposed method could be reduced to 113 in the Iris data set when the convergence curve was tested. When the calculation time was tested, the calculation time of the research method was only 2697ms when the sample size reached 10000. When the accuracy and purity tests were carried out, the accuracy and purity of the research method were 87.7% and 87.6% in the MR Dataset. In the Leaf dataset, the standardized mutual information index reached 0.6837 and the FM index reached 0.3903. The lowest Davies-Bouldin index was 0.71. The area enclosed by the receiver operation characteristic curve and the horizontal coordinate of the research method was the largest. The results indicate that the research method has high accuracy and computational efficiency in data anomaly detection and can provide effective technical references for anomaly detection. © 2024 Slovene Society Informatika. All rights reserved.

引用

页码：171 / 182

页数：11

共 21 条

[1]

Zuo Y., Wu Y., Min G., Huang C., Pe K., An intelligent anomaly detection scheme for micro-services architectures with temporal and spatial data analysis, IEEE Transactions on Cognitive Communications and Networking, 6, 2, pp. 548-561

[2]

Blazquez-Garcia A., Conde A., Mori U., Lozano J. A., A review on outlier/anomaly detection in time series data, ACM Computing Surveys (CSUR), 54, 3, pp. 1-33, (2021)

[3]

Zhang Y. M., Wang H., Wan H. P., Mao J. X., Xu Y. C., Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model, Structural Health Monitoring, 20, 6, pp. 2936-2952, (2021)

[4]

Sabic E., Keeley D., Henderson B., Nannemann S., Healthcare and anomaly detection: using machine learning to predict anomalies in heart rate data, Ai and Society, 36, 1, pp. 149-158, (2021)

[5]

Raja P. M. S., Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach, Biocybernetics and Biomedical Engineering, 40, 1, pp. 440-453, (2020)

[6]

Adamsa S. O., Azikweb E., Zubaira M. A., Artificial neural network analysis of some selected kdd cup 99dataset for intrusion detection, Acta Informatica Malaysia, 6, 2, pp. 55-61, (2022)

[7]

Ni F. T., Zhang J., Noori M. N., Deep learning for data anomaly detection and data compression of a long‐span suspension bridge, Computer-Aided Civil and Infrastructure Engineering, 35, 7, pp. 685-700, (2020)

[8]

Zhou X., Hu Y., Liang W., Ma J., Jin Q., Variational LSTM enhanced anomaly detection for industrial big data, IEEE Transactions on Industrial Informatics, 17, 5, pp. 3469-3477, (2020)

[9]

Mao J., Wang H., Spencer B. F., Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders, Structural Health Monitoring, 20, 4, pp. 1609-1626, (2021)

[10]

Liu Y., Garg S., Nie J., Zhang Y., Xiong Z., Kang J., Hossain M. S., Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach, IEEE Internet of Things Journal, 8, 8, pp. 6348-6358, (2020)

← 1 2 3 →