Network Intrusion Detection Method Based on Improved CNN in Internet of Things Environment

Wang, Yulin; Wang, Jinheng; Jin, Honglin

doi:10.1155/2022/3850582

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…However, since shallow machine learning methods are susceptible to spoofing attacks and are difficult to adapt to complex network attacks, more and more researchers are utilizing the adaptive and characterization capabilities of deep learning to automatically learn effective feature representations and perform pattern recognition from high-dimensional and massive network traffic data to improve the performance of intrusion detection models. Researchers have recently applied numerous deep learning techniques in intrusion detection scenarios within the industrial internet field, such as Convolutional Neural Networks (CNN [8,9]), Temporal Convolutional Networks (TCN [10]), Generative Adversarial Networks (GAN [11,12]), Long Short-Term Memory Networks (LSTM [13,14]), and some related combinations (CNN-LSTM [15]) aiming at further capturing the feature representations of network traffic.…”

Section: Introductionmentioning

confidence: 99%

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

Liu

2024

CMC

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Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multiscale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for industrial internet intrusion detection. First, we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic. Meanwhile, we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model's performance in capturing the multi-scale temporal features of network traffic. In addition, we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch. Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%, respectively, which also validates the effectiveness of enhancing the model's ability to capture multi-scale temporal features of traffic data on detection performance.

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Section: Introductionmentioning

confidence: 99%

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

Liu

2024

CMC

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“…In the same way, we compare the performance of EFedID with some of the latest other notable state-of-the-art methods. Models CNN [37] and NIDS-CNNLSTM [38] were both trained using the KDDTrain + dataset and tested using the KDDTest + dataset. Te model SMOTE-GAN-VAE [39] was evaluated on the NSL-KDD and CICIDS 2017 datasets, respectively.…”

Section: Performance Comparison With Other Latest Methodsmentioning

confidence: 99%

“…A similar trend is observed in the KDD CUP 99 dataset, where EFedID with c � 0.7 attains the highest accuracy of 0.9735, followed by EFedID with c � 0.5 (accuracy of 0.9712) and EFedID with c � 0.3 (accuracy of 0.9706). Once again, all models with varying c values CNN [37] 92.14 Tree-stage SMOTE-GAN-VAE [39] 94.0 NIDS-CNNLSTM [38] 97.05 EFedID 97.3…”

Section: Impact Of Cmentioning

confidence: 99%

Efficient Privacy-Preserving Federated Deep Learning for Network Intrusion of Industrial IoT

He,

Zhang,

Wang

et al. 2023

International Journal of Intelligent Systems

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Intrusion detection systems play a very important role in industrial Internet network security. However, in the large-scale, complex, and heterogeneous industrial Internet of Things (IoT), it is becoming more and more difficult to defend network intrusion threats due to the insufficiency of high-quality attack samples. To solve the problem, an efficient federated network intrusion method called EFedID is proposed for industrial IoT, which can allow different industrial agents to collaboratively train a comprehensive detection model. Specifically, the adaptive gradient sparsification method is introduced to alleviate the communication and computation overheads. To protect the data privacy of the agents, a CKKS cryptosystem-based secure communication protocol is designed to encrypt the model parameters through the federated training process. Our proposed system demonstrates exceptional detection performance on the NSL-KDD, KDD CUP 99, and CICIDS 2017 datasets. Notably, on the NSL-KDD dataset, the model compression rate reaches 9 times while the model accuracy reaches 84.31%. On the KDD CUP 99 dataset, the model compression rate reaches 8.9 times while the model accuracy reaches 97.3%. Lastly, on the CICIDS 2017 dataset, the model compression rate reached 6.173 times while the model accuracy reached 95.51%. The experimental results demonstrate that the proposed method is very suitable for effectively developing a high-accuracy detection model while protecting the data information of industrial agents. Furthermore, the method can be extended to other recent deep learning networks for intrusion detection.

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“…Moreover, they stated that IoT encounters many significant challenges in this era of vast amounts of data and information: large quantities of data redundancy, bottlenecks in cloud processing power, data security, and privacy. In the related research on intrusion detection techniques, it was found that most intrusion detection techniques fail to meet the actual demand standards of IoT, and there are also problems of poor detection of complex network intrusion methods [8].…”

Section: Introductionmentioning

confidence: 99%

A Review of Joint Applications of IoT and Deep Learning

Tang,

Wang

2023

IETI Trans. Data Anal. Forecast.

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In recent years, graph convolutional networks (GCNs) have been widely used in image classification tasks. The combination of GCNs and the Internet of Things (IoT) has led to the development of some branches of the latter. This paper explores cases where convolutional neural networks and GCNs are combined with IoT to achieve better results. This paper also focuses on discussing the semi-supervised classification task of GCNs. The innovative approach explored for innovative GCNs dealing with semi-supervised classification tasks lies in optimizing the GCN topology and using graph convolutional operations in the topological space for better training of the model.

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Network Intrusion Detection Method Based on Improved CNN in Internet of Things Environment

Cited by 10 publications

References 27 publications

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

Efficient Privacy-Preserving Federated Deep Learning for Network Intrusion of Industrial IoT

A Review of Joint Applications of IoT and Deep Learning

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