Attack Traffic Detection Based on LetNet-5 and GRU Hierarchical Deep Neural Network

Wang, Zitian; Wang, Zesong; Yi, FangZhou; Zeng, Cheng

doi:10.1007/978-3-030-86137-7_36

“…The gated recurrent unit (GRU) [14] is also a variant of the recurrent neural network, whose main purpose is also to deal with the gradient disappearance and explosion phenomenon occurring during the long sequence of the training process. Since the structures of the GRU and the LSTM are very similar, the GRU can be seen as a variant of the LSTM.…”

Section: The Model Of Grumentioning

confidence: 99%

An industrial network intrusion detection algorithm based on IGWO-GRU

Yang,

Shan,

Wang

et al. 2024

Cluster Comput

0

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Nowadays, the industrial control system has become open and interconnected, and informatization also increases the risk of network attacks and damage due to frequent intrusion. Research on industrial intrusion detection is ongoing, but many current methods need to consider the characteristics of industrial control flow. Therefore, this paper proposes an industrial network intrusion detection algorithm based on IGWO-GRU: starting from the timing of industrial control network traffic, select the simple architecture of the gated recurrent unit (GRU) as the network model; in view of the problem of the number of network parameters such as neurons and the learning rate, the Grey Wolf Optimizer (GWO) is integrated with conducting autonomous learning to find the optimal parameters of the model and solve the problem of slow convergence rate caused by a large amount of data volume of the industrial control network traffic. However, due to the slow convergence speed and low optimization accuracy of the GWO algorithm and data imbalance, this paper improves an improved grey Wolf optimization algorithm (IGWO) by improving the nonlinear convergence factor and weight adjustment strategy to increase the convergence rate of the algorithm further and avoid falling into the local optimal solution. With the data set of the natural gas pipeline control system, the intrusion detection system is simulated for classifying abnormal flow attacks. The experimental results show that the IGWO-GRU algorithm has obvious advantages in accuracy, false alarm rate, and false report rate, which improves the safety protection ability of industrial control systems.

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An Industrial Network Intrusion Detection Algorithm Based on IGWO-GRU

yang,

shan,

wang

et al. 2023

Preprint

0

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Nowadays, the industrial control system has become open and interconnected, and informatization also increases the risk of network attacks and damage due to frequent intrusion. Research on industrial intrusion detection is ongoing, but many current methods need to consider the characteristics of industrial control flow. Therefore, this paper proposes an industrial network intrusion detection algorithm based on IGWO-GRU: starting from the timing of industrial control network traffic, select the simple architecture of the gated recurrent unit (GRU) as the network model; in view of the problem of the number of network parameters such as neurons and the learning rate, the Grey Wolf Optimizer (GWO) is integrated with conducting autonomous learning to find the optimal parameters of the model and solve the problem of slow convergence rate caused by a large amount of data volume of the industrial control network traffic. However, due to the slow convergence speed and low optimization accuracy of the GWO algorithm and data imbalance, this paper improves an improved grey Wolf optimization algorithm (IGWO) by improving the nonlinear convergence factor and weight adjustment strategy to increase the convergence rate of the algorithm further and avoid falling into the local optimal solution. With the data set of the natural gas pipeline control system, the intrusion detection system is simulated for classifying abnormal flow attacks. The experimental results show that the IGWO-GRU algorithm has obvious advantages in accuracy, false alarm rate, and false report rate, which improves the safety protection ability of industrial control systems.

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Network Traffic Anomaly Detection Based on Spatiotemporal Feature Extraction and Channel Attention

Ji,

Yu,

Dai

2024

Processes

0

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To overcome the challenges of feature selection in traditional machine learning and enhance the accuracy of deep learning methods for anomaly traffic detection, we propose a novel method called DCGCANet. This model integrates dilated convolution, a GRU, and a Channel Attention Network, effectively combining dilated convolutional structures with GRUs to extract both temporal and spatial features for identifying anomalous patterns in network traffic. The one-dimensional dilated convolution (DC-1D) structure is designed to expand the receptive field, allowing for comprehensive traffic feature extraction while minimizing information loss typically caused by pooling operations. The DC structure captures spatial dependencies in the data, while the GRU processes time series data to capture dynamic traffic changes. Furthermore, the channel attention (CA) module assigns importance-based weights to features in different channels, enhancing the model’s representational capacity and improving its ability to detect abnormal traffic. DCGCANet achieved an accuracy rate of 99.6% on the CIC-IDS-2017 dataset, outperforming other algorithms. Additionally, the model attained precision, recall, and F1 score rates of 99%. The generalization capability of DCGCANet was validated on a subset of CIC-IDS-2017, demonstrating superior detection performance and robust generalization potential.

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Attack Traffic Detection Based on LetNet-5 and GRU Hierarchical Deep Neural Network

Cited by 3 publications

References 12 publications

An industrial network intrusion detection algorithm based on IGWO-GRU

An industrial network intrusion detection algorithm based on IGWO-GRU

An Industrial Network Intrusion Detection Algorithm Based on IGWO-GRU

Network Traffic Anomaly Detection Based on Spatiotemporal Feature Extraction and Channel Attention

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