RICNN: A ResNet&amp;Inception convolutional neural network for intrusion detection of abnormal traffic

Xia, Benhui; Han, Dong‐Guk; Yin, Ximing

doi:10.2298/csis210617055x

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…The essence of the research results lies in their specific contributions to wind farm operation, maintenance, and management practice. The high-speed convergence of the model implies that new data can be adapted more quickly in real-time fault monitoring and early warning systems, which is particularly important for dynamic wind farm operating environments [23]. In addition, its high accuracy rate indicates that the frequency of false alarms can be significantly reduced, and by accurately predicting potential failure points, it can provide valuable decision support for wind farm maintenance personnel, reduce unnecessary inspection and maintenance costs, and improve the safety and efficiency of wind farms.…”

Section: Discussionmentioning

confidence: 99%

“…Field-of-view enhancement, on the other hand, improves the model's ability to capture anomalies in wind farms. Fault diagnosis and prevention lie in the ability to identify and learn from small changes in the operation of wind turbines, which often indicate potential failures [23]. The design of the RFECNN allows it to understand these changes more deeply, enabling accurate early warning of potential problems.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Li,

Wang,

Qin

et al. 2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Li,

Wang,

Qin

et al. 2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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show abstract

“…Abnormal traffic detection typically always relies on spatial and temporal features, as well as a combination of both. Spatial features are usually extracted using Convolutional Neural Network (CNN) [14][15][16]. Li et al [14] proposed a multi-CNN fusion method.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Su,

Wang,

et al. 2024

CMC

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Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more difficult. To address these issues, this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection. To fully utilize the multi-scale information in network traffic, a Multi-scale Dilated feature extraction (MD) block is introduced. This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model's receptive field. The proposed Max-feature-map Residual with Dual-channel pooling (MRD) block integrates the maximum feature map with the residual block. This module ensures the model focuses on key information, thereby optimizing computational efficiency and reducing unnecessary information redundancy. Experimental results show that compared to the latest methods, the proposed abnormal traffic detection model improves accuracy by about 2%.

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“…SVM has been successfully applied to image recognition [6]. In machine learning, SVM can avoid the complexity of high-dimensional space, and it is very prominent in small sample, high-dimensional space calculation and nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

A novel deep LeNet-5 convolutional neural network model for image recognition

Zhang

Lei

et al. 2022

COMSIS J

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At present, the traditional machine learning methods and convolutional neural network (CNN) methods are mostly used in image recognition. The feature extraction process in traditional machine learning for image recognition is mostly executed by manual, and its generalization ability is not strong enough. The earliest convolutional neural network also has many defects, such as high hardware requirements, large training sample size, long training time, slow convergence speed and low accuracy. To solve the above problems, this paper proposes a novel deep LeNet-5 convolutional neural network model for image recognition. On the basis of Lenet-5 model with the guaranteed recognition rate, the network structure is simplified and the training speed is improved. Meanwhile, we modify the Logarithmic Rectified Linear Unit (L_ReLU) of the activation function. Finally, the experiments are carried out on the MINIST character library to verify the improved network structure. The recognition ability of the network structure in different parameter s is analyzed compared with the state-of-the-art recognition algorithms. In terms of the recognition rate, the proposed method has exceeded 98%. The results show that the accuracy of the proposed structure is significantly higher than that of the other recognition algorithms, which provides a new reference for the current image recognition.

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RICNN: A ResNet&Inception convolutional neural network for intrusion detection of abnormal traffic

Cited by 5 publications

References 40 publications

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

A novel deep LeNet-5 convolutional neural network model for image recognition

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