Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection

Ding, Pengpeng; Li, Jinguo; Wen, Mei; Wang, Liangliang; Li, Hongjiao

doi:10.1109/access.2020.3022355

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…They used a stack autoencoder to reduce the dimension of data feature and then combined SVM and an artificial bee colony algorithm to perform an intrusion detection experiment. Although machine-learning-based methods have achieved good results in recent years, they can only perform shallow learning and cannot accurately identify network attacks in ICSs [14].…”

Section: Intrusion Detection Methods Based On Machine Learningmentioning

confidence: 99%

One-Dimensional Convolutional Wasserstein Generative Adversarial Network Based Intrusion Detection Method for Industrial Control Systems

Cai,

Du,

Wang

et al. 2023

Electronics

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The imbalance between normal and attack samples in the industrial control systems (ICSs) network environment leads to the low recognition rate of the intrusion detection model for a few abnormal samples when classifying. Since traditional machine learning methods can no longer meet the needs of increasingly complex networks, many researchers use deep learning to replace traditional machine learning methods. However, when a large amount of unbalanced data is used for training, the detection performance of deep learning decreases significantly. This paper proposes an intrusion detection method for industrial control systems based on a 1D CWGAN. The 1D CWGAN is a network attack sample generation method that combines 1D CNN and WGAN. Firstly, the problem of low ICS intrusion detection accuracy caused by a few types of attack samples is analyzed. This method balances the number of various attack samples in the data set from the aspect of data enhancement to improve detection accuracy. According to the temporal characteristics of network traffic, the algorithm uses 1D convolution and 1D transposed convolution to construct the modeling framework of network traffic data of two competing networks and uses gradient penalty instead of weight cutting in the Wasserstein Generative Adversarial Network (WGAN) to generate virtual samples similar to real samples. After a large number of data sets are used for verification, the experimental results show that the method improves the classification performance of the CNN and BiSRU. For the CNN, after data balancing, the accuracy rate is increased by 0.75%, and the accuracy, recall rate and F1 are improved. Compared with the BiSRU without data processing, the accuracy of the s1D CWGAN-BiSRU is increased by 1.34%, and the accuracy, recall and F1 are increased by 7.2%, 3.46% and 5.29%.

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Section: Intrusion Detection Methods Based On Machine Learningmentioning

confidence: 99%

One-Dimensional Convolutional Wasserstein Generative Adversarial Network Based Intrusion Detection Method for Industrial Control Systems

Cai,

Du,

Wang

et al. 2023

Electronics

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“…Ding used an intrusion detection model combining CNN and BiSRU to achieve accurate prediction of network intrusion [15]. Ding proposed an effective model for network security protection using BiSRU in conjunction with feature reduction for identifying anomalous traffic [16].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Liu

Hassan

et al. 2023

IEEE Access

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Dissolved oxygen content is a key indicator of water quality in aquaculture environment. Because of its nonlinearity, dynamics, and complexity, which makes traditional methods face challenges in the accuracy and speed of dissolved oxygen content prediction. As a solution to these issues, this study introduces a hybrid model consisting of the Light Gradient Boosting Machine (LightGBM) and the Bidirectional Simple Recurrent Unit (BiSRU). Firstly, Linear interpolation and smoothing were used to identify significant parameters. LightGBM algorithm then determines the significance of dissolved oxygen by eliminating irrelevant variables and predicting dissolved oxygen in intensive aquaculture. Finally, the attention method was implemented to map the weighting and learning parameter matrices, so enabling the BiSRU's hidden states to be assigned different weights. The findings shown that the presented prediction model can accurately anticipate the fluctuating trend of dissolved oxygen over a 10-day period in just 122 seconds, and the accuracy rate reached 96.28%. Comparing the model effects of LightGBM -BiSRU, LightGBM -GRU, LightGBM-LSTM, and BiSRU -Attention takes the least time. Its higher prediction accuracy can provide an essential reference for intensive aquaculture water quality regulation.

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“…The problem of campus network traffic anomaly detection is a large-scale traffic anomaly detection problem, and the efficiency and cost of anomaly detection should be considered in addition to the accuracy of detection when performing detection on campus networks [5]. In this regard, literature [6] proposed a network intrusion detection model based on wavelet neural network; literature [7] proposed a traffic anomaly detection method combining feature dimensionality reduction; and literature [8] proposed an active intrusion detection framework based on density perception and feature deviation of network traffic flow. However, the existing research has not solved these problems well.…”

Section: Introductionmentioning

confidence: 99%

Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic

Zhao,

2023

Journal of Applied Mathematics

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With the continuous development of information technology and the continuous progress of traffic bandwidth, the types and methods of network attacks have become more complex, posing a great threat to the large-scale campus network environment. To solve this problem, a network traffic anomaly detection model based on subspace information entropy flow matrix and a subspace anomaly weight clustering network traffic anomaly detection model combined with density anomaly weight and clustering ideas are proposed. Under the two test sets of public dataset and collected campus network data information of a university, the detection performance of the proposed anomaly detection method is compared with other anomaly detection algorithm models. The results show that the proposed detection model is superior to other models in speed and accuracy under the open dataset. And the two traffic anomaly detection models proposed in the study can well complete the task of network traffic anomaly detection under the large-scale campus network environment.

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Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection

Cited by 12 publications

References 36 publications

One-Dimensional Convolutional Wasserstein Generative Adversarial Network Based Intrusion Detection Method for Industrial Control Systems

One-Dimensional Convolutional Wasserstein Generative Adversarial Network Based Intrusion Detection Method for Industrial Control Systems

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic

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