Three-Dimensional Convolutional Neural Network Based Traffic Classification for Wireless Communications

Ran, Jing; Chen, Yexin; Li, Shulan

doi:10.1109/globalsip.2018.8646659

Cited by 22 publications

(9 citation statements)

References 14 publications

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“…Although it is of great quality, few relevant studies use this database for traffic analysis alone. More commonly, it was utilized as a supplementary dataset for intrusion detection by Zhao [35], Tang [36], Ran [37]. Table 16 presents the performance comparison for the USTC-TFC2016 dataset as a complete dataset.…”

Section: Comparison With Other Published Methodsmentioning

confidence: 99%

A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection

Yang

Zhang

et al. 2020

IEEE Access

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As an essential part of the network-based intrusion detection systems (IDS), malicious traffic detection using deep learning methods has become a research focus in network intrusion detection. However, even the most advanced IDS available are challenging to satisfy real-time detection because they usually need to accumulate the packets into particular flows and then extract the features, causing processing delays. In this paper, using the deep learning approach, we propose a deep hierarchical network for malicious traffic detection at the packet-level, capable of learning the features of traffic from raw packet data. It used the one-dimensional convolutional layer to extract the spatial features of raw packets and Gated Recurrent Units (GRU) structure to extract the temporal features. To evaluate the performance of our approach, experiments were conducted to examine the efficiency of the proposed deep hierarchical network based on the ISCX2012 dataset, USTC-TFC2016 dataset and CICIDS2017 dataset, respectively. Accuracy (ACC), detection rate (DR) and false alarm rate (FAR) are the metrics for evaluation. In the ISCX2012 dataset, our approach achieved 99.42%, 99.74%, 1.77% on ACC, DR and FAR, respectively. In USTC-TFC2016, there were 99.94%, 99.99%, 0.99%. In CICIDS2017, there were 100%, 100%, 0%. Furthermore, we discussed the impact of data balanced on classification performance and the time efficiency between the Long Short-Term Memory (LSTM) model and the GRU model. Experiments show that our approach can effectively detect malicious traffic and outperforms many other state-of-the-art methods in terms of ACC and DR.

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Section: Comparison With Other Published Methodsmentioning

confidence: 99%

A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection

Yang

Zhang

et al. 2020

IEEE Access

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“…One approach that was taken to improve classification accuracy further was to artificially expand the training data by displacing the training image by one pixel (up or down, left or right). Rectified linear units (ReLU) have been used to activate neurons in various layers of the DenseNet instead of the sigmoid activation function due to their generally recognized improved performance [4,5].…”

Section: Methodsmentioning

confidence: 99%

“…Convolutional neural networks (CNNs) have been used extensively for image classification and recognition over the years [3][4][5][6][7][8]. However, like other neural network structures, CNNs are also susceptible to problems of false classification due to inaccurate feature selection.…”

Section: Introductionmentioning

confidence: 99%

Improved Predictive Sparse Decomposition Method With Densenet for Prediction of Lung Cancer

Mienye¹,

Sun²,

Wang³

2020

IJC

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Lung cancer is the second most common form of cancer in both men and women. It is responsible for at least 25% of all cancer-related deaths in the United States alone. Accurate and early diagnosis of this form of cancer can increase the rate of survival. Computed tomography (CT) imaging is one of the most accurate techniques for diagnosing the disease. In order to improve the classification accuracy of pulmonary lesions indicating lung cancer, this paper presents an improved method for training a densely connected convolutional network (DenseNet). The optimized setting ensures that code prediction error and reconstruction error within hidden layers are simultaneously minimized. To achieve this and improve the classification accuracy of the DenseNet, we propose an improved predictive sparse decomposition (PSD) approach for extracting sparse features from the medical images. The sparse decomposition is achieved by using a linear combination of basis functions over the L2 norm. The effect of dropout and hidden layer expansion on the classification accuracy of the DenseNet is also investigated. CT scans of human lung samples are obtained from The Cancer Imaging Archive (TCIA) hosted by the University of Arkansas for Medical Sciences (UAMS). The proposed method outperforms seven other neural network architectures and machine learning algorithms with a classification accuracy of 95%.

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“…The paper uses CNN for spatial feature learning, LSTM [29] for time-domain feature learning, SAE for coding feature learning, and finally combines these three aspects of features to enhance the understanding of the original input data. Ran et al [30] first propose the application of 3D CNN networks for traffic classification. All these deep learning based methods for encrypted traffic classification have achieved good results.…”

Section: Related Workmentioning

confidence: 99%

Detection and Forensics of Encryption Behavior of Storage File and Network Transmission Data

Liu

2020

IEEE Access

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In recent years, with the widespread application of encryption technology, criminals can hide malicious data without being discovered by security regulatory authorities, which has brought serious challenges to computer forensic investigation. Therefore, it is urgent to study the technology of detection and forensics of encrypted data. This paper proposes a method for encryption detection based on a deep convolutional neural network. The method first converts the raw data into two-dimensional matrixes as the input of the convolutional neural network. Then, the multiscale feature extraction mechanism with multiple activation functions is utilized to provide representative features as the input of subsequent layers. Next, the residual learning operation can further enhance the discrimination of features. By this mean, a network which can automatically extract and learn global contextual information of encrypted data is constructed. The experiment results show that the proposed method achieves high accuracy in the detection of storage file and network transmission data compare to the competitive methods and the detection accuracy on different types of mixed data is higher than 99%. Moreover, the proposed method can accurately detect data encrypted with different algorithms. The average detection rate of DES-encrypted data is higher than that of competitors by more than 5%.INDEX TERMS encryption detection and forensics, storage file, network transmission data, deep learning

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Three-Dimensional Convolutional Neural Network Based Traffic Classification for Wireless Communications

Cited by 22 publications

References 14 publications

A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection

A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection

Improved Predictive Sparse Decomposition Method With Densenet for Prediction of Lung Cancer

Detection and Forensics of Encryption Behavior of Storage File and Network Transmission Data

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