Network intrusion detection based on BiSRU and CNN

Ding, Shanshuo; Wang, Yingxin; Kou, Liang

doi:10.1109/mass52906.2021.00026

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…The methods we used in our comparison were as follows: SVM [36], which uses support vector machines, a traditional machine learning method, to classify packets as trusted or malicious; CNN [37], which uses only convolutional neural network performs spatial feature extraction and uses the loss function and redundant error items designed by the spatial characteristics of the link load to achieve intrusion detection; LSTM [38], which only uses long short-term Memory network training to obtain the characteristics of traffic data changes in the time dimension In order to achieve intrusion detection; and CNN-BiSRU [39], which uses two deep learning models for serial feature extraction, with the first being convolutional neural network to extract the spatial features of the original data and the other being bidirectional simple recurrent unit to extract the temporal features based on it. Finally, the classification results were output through softmax to achieve the purpose of intrusion detection.…”

Section: Performance Comparisonmentioning

confidence: 99%

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

Xing

Wang

et al. 2023

Sensors

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The problems with network security that the Internet of Vehicles (IoV) faces are becoming more noticeable as it continues to evolve. Deep learning-based intrusion detection techniques can assist the IoV in preventing network threats. However, previous methods usually employ a single deep learning model to extract temporal or spatial features, or extract spatial features first and then temporal features in a serial manner. These methods usually have the problem of insufficient extraction of spatio-temporal features of the IoV, which affects the performance of intrusion detection and leads to a high false-positive rate. To solve the above problems, this paper proposes an intrusion detection method for IoV based on parallel analysis of spatio-temporal features (PA-STF). First, we built an optimal subset of features based on feature correlations of IoV traffic. Then, we used the temporal convolutional network (TCN) and long short-term memory (LSTM) to extract spatio-temporal features in the IoV traffic in a parallel manner. Finally, we fused the spatio-temporal features extracted in parallel based on the self-attention mechanism and used a multilayer perceptron to detect attacks in the Internet of Vehicles. The experimental results show that the PA-STF method reduces the false-positive rate by 1.95% and 1.57% on the NSL-KDD and UNSW-NB15 datasets, respectively, with the accuracy and F1 score also being superior.

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Section: Performance Comparisonmentioning

confidence: 99%

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

Xing

Wang

et al. 2023

Sensors

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“…For example, Jie used a prediction method based on BiSRU to achieve high accuracy prediction for intrusion detection of industrial control systems [14]. 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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Feature Ranking using Statistical Techniques for Computer Networks Intrusion Detection

Sharma

Arora

2022

2022 7th International Conference on Communication and Electronics Systems (ICCES)

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Network intrusion detection based on BiSRU and CNN

Cited by 5 publications

References 3 publications

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features

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

Feature Ranking using Statistical Techniques for Computer Networks Intrusion Detection

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