TLE outlier detection based on expectation maximization algorithm

Liu, Jinghong; Liu, Lei; Du, Jianli; Sang, Jizhang

doi:10.1016/j.asr.2021.07.013

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…This approach is widely popular due to the ease of implementation. Another method is discriminative training, which trains by normalizing a mixed model and maximizing a prior probability ( | ) k p C y to draw accurate decision boundaries [22][23]. In classification problems with K classes, the conditional density of each class is modeled using M Gaussian distributions, as shown in equation (10).…”

Section: Incomplete Datamentioning

confidence: 99%

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Network Traffic Identification Based on Improved EM Algorithm

Cui,

Liang,

Wang

2024

IEEE Access

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With the continuous increase and complexity of network traffic, traditional network traffic recognition technology is facing numerous difficulties, especially in dealing with outlier data and improving recognition accuracy. Therefore, an improved expectation maximization algorithm based on the constraint matrix Z and Tsallis entropy is proposed. The core goal of this algorithm is to accelerate the convergence of classification and improve accuracy. Furthermore, to enhance the classification accuracy, the spatial expectation maximization algorithm is introduced, which innovatively converts the sample mean and covariance matrix into 1 L -median and modified rank covariance matrix. According to the experimental data, therecall rate of the original expectation maximization algorithm is only 74%. However, the recall rate of the spatial expectation maximization algorithm in the ATTACK service has significantly increased to 85%.In other tests, such as WWW and P2P services, the recall rate has also significantly improved, increasing from 96% and 95.3% to 97.7% and 96.1%, respectively. These experimental results highlight the superior robustness of the spatial expectation maximization algorithm in handling outlier data. It further proves the outstanding performance in improving the accuracy of network traffic recognition. This research has brought significant innovation and potential practical value to the network traffic identification.

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Section: Incomplete Datamentioning

confidence: 99%

“…, ( ) C y is the sum of the distances from point y to all points in set X . This ensures that the total cost of providing services to all n points by the service facility is minimized, as shown in equation (23).…”

Section: X-axis Y-axismentioning

confidence: 99%

Network Traffic Identification Based on Improved EM Algorithm

Cui,

Liang,

Wang

2024

IEEE Access

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“…However, the threshold set often had errors, and an accurate dividing line between abnormal and normal data cannot be obtained. Liu et al [4] proposed a novel filter based on an expectation-maximization model to identify anomalies in time-series data. The disadvantage of this model was the absolute dependence on the abnormal threshold.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks

et al. 2022

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Because the linear motor feeding system always runs in complex working conditions for a long time, its performance and state transition have great randomness. Therefore, abnormal detection is particularly significant for predictive maintenance to promptly discover the running state degradation trend. Aiming at the problem that the abnormal samples of linear motor feed system are few and the samples have time-series features, a method of abnormal operation state detection of a linear motor feed system based on normal sample training was proposed, named GANomaly-LSTM. The method constructs an encoding-decoding-reconstructed encoding network model. Firstly, the time-series features of vibration, current and composite data samples are extracted by the long short-term memory (LSTM) network; Secondly, the three-layer fully connected layer is employed to extract potential feature vectors; Finally, anomaly detection of the system is completed by comparing the potential feature vectors of the two encodings. An experimental platform of the X-Y two-axis linkage linear motor feeding system is built to verify the rationality of the proposed method. Compared with other classical methods such as GANomaly and GAN-AE, the average AUROC index of this method is improved by 17.5% and 9.3%, the average accuracy is enhanced by 11.6% and 15.5%, and the detection time is shortened by 223 ms and 284 ms, respectively. GANomaly-LSTM has successfully proved its superiority for abnormal detection for running state of linear motor feeding systems.

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