Classification for Multiple Power Quality Disturbances Based on Deep Forest

Xin, Kaihua; Duan, Bin; Qu, Xiangshuai

doi:10.1109/iecon.2019.8926759

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…In addition, for the multiclassification case, since the number of training samples of each class will be less, a detection model based on a deep-learning method will face the same problem. Deep forest is a multilayer model based on a decision tree ensemble that has been used in the field of image classification, and has proved suitable for small-scale and unbalanced data detection [7,8]. Therefore, aiming at the high-precision detection of SSL/TLS-encrypted malicious traffic, we proposed a DF-IDS method based on deep forest for small-scale and unbalanced data.…”

Section: Introductionmentioning

confidence: 99%

Deep-Forest-Based Encrypted Malicious Traffic Detection

et al. 2022

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The SSL/TLS protocol is widely used in data encryption transmission. Aiming at the problem of detecting SSL/TLS-encrypted malicious traffic with small-scale and unbalanced training data, a deep-forest-based detection method called DF-IDS is proposed in this paper. According to the characteristics of SSL/TSL protocol, the network traffic was split into sessions according to the 5-tuple information. Each session was then transformed into a two-dimensional traffic image as the input of a deep-learning classifier. In order to avoid information loss and improve the detection efficiency, the multi-grained cascade forest (gcForest) framework was simplified with only cascade structure, which was named cascade forest (CaForest). By integrating random forest and extra trees in the CaForest framework, an end-to-end high-precision detector for small-scale and unbalanced SSL/TSL encrypted malicious traffic was realized. Compared with other deep-learning-based methods, the experimental results showed that the detection rate of DF-IDS was 6.87% to 29.5% higher than that of other methods on a small-scale and unbalanced dataset. The advantage of DF-IDS was more obvious in the multi-classification case.

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Section: Introductionmentioning

confidence: 99%

Deep-Forest-Based Encrypted Malicious Traffic Detection

et al. 2022

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“…A significant part of such a plan involves the massive diffusion and installation of renewable energy sources (RES). However, one of the drawbacks of their diffusion is the increase in grid-injected disturbances, and hence of power quality (PQ) issues [ 2 , 3 , 4 , 5 ], that may alter the correct operation of the grid.…”

Section: Introductionmentioning

confidence: 99%

On the Long-Period Accuracy Behavior of Inductive and Low-Power Instrument Transformers

Mingotti

Bartolomei

Peretto

et al. 2020

Sensors

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The accuracy evaluation of instrument transformers is always a key task when proper control and management of the power network is required. In particular, accuracy becomes a critical aspect when the grid or the instrumentation itself is operating at conditions different from the rated ones. However, before focusing on the above non-rated conditions, it is important to fully understand the instrument transformer behavior at rated conditions. To this end, this work analyzed the accuracy behavior of legacy, inductive, and low-power voltage transformers over long periods of time. The aim was to find patterns and correlations that may be of help during the modelling or the output prediction of voltage transformers. From the results, the main differences between low-power and inductive voltage transformers were pointed out and described in detail.

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Classification for Multiple Power Quality Disturbances Based on Deep Forest

Cited by 2 publications

References 5 publications

Deep-Forest-Based Encrypted Malicious Traffic Detection

Deep-Forest-Based Encrypted Malicious Traffic Detection

On the Long-Period Accuracy Behavior of Inductive and Low-Power Instrument Transformers

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