A Novel Intrusion Detection Scheme Using Cloud Grey Wolf Optimizer

Yang, Honghao; Zhou, Zhiping

doi:10.23919/chicc.2018.8483324

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Table 1 is the confusion matrix [21] used, the horizontal is the judgment result of anomaly detection, and the vertical is the true label of the sample in the test set. Accuracy: This indicator indicates the proportion of the number of samples that are abnormal in the detection and the actual abnormal situation to the total number of abnormal samples in the detection, which is calculated as shown in Equation (6). 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦…”

Section: Experiments Setup and Evaluation Metricsmentioning

confidence: 99%

“…In addition, a large amount of data generated by the device is unlabeled and private, which makes it difficult for supervised methods. Therefore, unsupervised learning algorithms have been proposed in anomaly detection of devices, such as clustering algorithms [2,3,4], Gaussian mixture model [5], single-classification support vector machine [6,7], generative adversarial network (GAN) [8,9], isolated forest [10,11], reduction and reconstruction method [12], and federated deep learning [13,14]. Among them, literature [2] describes the clustering genetic optimization anomaly detection method based on the idea of density to detect the abnormal situation of energy storage batteries, designs an effective objective function to describe the abnormal state of data, and realizes the abnormal detection of abnormal data.…”

Section: Introductionmentioning

confidence: 99%

“…6, and the integrated development tools used were NetLogo and Pycharm. The data processing part mainly uses Pandas, Numpy, and Sklearn packages for calculation, and the neural network part uses open-source deep learning Tensorflow2 and Keras framework to jointly build a deep learning network architecture and uses Matplotlib and Seaborn modules to generate visual images for easy analysis.…”

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confidence: 99%

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Photovoltaic inverter anomaly detection method based on LSTM serial depth autoencoder

Dong

Huang

et al. 2023

J. Phys.: Conf. Ser.

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To ensure the safety of the massive growth of distributed photovoltaic grid-connected inverters and the security of backhaul data in the context of new power systems, research on anomaly detection has also been put on the agenda. The data of the photovoltaic grid-connected inverter has complex time dependence and uncertainty, and the data security problem is prone to occur in the process of data transmission, and the existing anomaly detection means do not comprehensively consider these factors, and cannot timely find the abnormal phenomena caused by the synthesis of data security and their faults, which leads to the tampering of power data and the increase of operation and maintenance costs. To solve the above problems, an anomaly detection method integrating a long short-term memory network (LSTM) and serial depth autoencoder (DAE) is proposed based on edge computing, characterized by the power and voltage of the device, the length, and the delay of the data. Firstly, the method performed time series transformation processing on the original training dataset through the sliding window. Then, the model was trained using LSTM and DAE. Finally, the decoding error of the test data was compared with the threshold obtained by the training. The example verification shows that the model has achieved good performance in the recall rate, accuracy rate and F1 score, and can effectively detect the equipment that produces its fault or data return abnormality in the photovoltaic inverter. So, the model has good industrial practical value.

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Section: Experiments Setup and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Photovoltaic inverter anomaly detection method based on LSTM serial depth autoencoder

Dong

Huang

et al. 2023

J. Phys.: Conf. Ser.

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“…e hyperentropy He is the entropy and a measure of uncertainty [21]. Hyperentropy associates fuzziness and randomness, reflecting the degree of dispersion of the cloud droplets, i.e., the thickness of the cloud [22,23], and extends the normal distribution to the generalised normal distribution [24].…”

Section: Preliminariesmentioning

confidence: 99%

Fuzzy Risk Assessment of Tunnels’ Undercrossing Existing Structures

Wang

2022

Mathematical Problems in Engineering

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Numerical calculation is usually employed to analyse the safety risks of tunnel construction under existing structures. However, due to the fuzziness and randomness of the construction environment, the simulation results fluctuate significantly. Given the fuzziness and randomness encountered during the safety risk assessment of tunnels undercrossing existing structures, this study analyses and summarises the previous experiences and selects a relevant algorithm of the cloud model to solve the uncertainty problem during the assessment. The study proposes three forms of cloud models to evaluate the risks of tunnels undercrossing existing structures and performs a comparative analysis between them. Finally, the effectiveness and feasibility of the cloud model calculation method are verified through a case study. The research shows that (1) the accuracy of the one-dimensional and two-dimensional cloud models was high, and the difference was small, indicating that the cloud model method used to determine the risk level of tunnels underpassing existing structures was reasonably effective, (2) since the soft ‘and’ two dimensions operated computation, the distribution probability was dispersed, making the soft ‘and’ computation more affected by the index, resulting in lower accuracy than the one-dimensional and two-dimensional cloud models, and (3) compared with numerical calculation, the cloud model calculation was more convenient, reflecting the relationship between the indices and the relationship between the indices and the complex whole. The evaluation results used the comprehensive evaluation probability to determine the risk level so that the evaluation results were reliable.

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