Empirical Mode Decomposition-empowered Network Traffic Anomaly Detection for Secure Multipath TCP Communications

Cao, Yuanlong; Ji, Ruiwen; Huang, Xin; Lei, Gang; Shao, Xun; You, Ilsun

doi:10.1007/s11036-022-02005-6

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…At the same time, it can also effectively suppress or eliminate complex environments under the influence of noise. It has certain research prospects in complex neural networks [27,28], traffic prediction models [29][30][31], and network security [32][33][34].…”

Section: Eemd Algorithmmentioning

confidence: 99%

Retracted: Anomaly detection with ensemble empirical mode decomposition and approximate entropy for quick user datagram protocol internet connection‐based distributed Blockchain systems

Cao

et al. 2023

IET Software

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With the vigorous development of mobile Internet and Blockchain, the rapid and secure interaction of high-frequency data in the era of data explosion has gradually become an urgent need for people. Quick User Datagram Protocol Internet Connection Protocol (QUIC) can be perfectly integrated into the Blockchain due to its low latency and high security, providing a safe and reliable transmission service for the Blockchain system. However, QUIC-based distributed Blockchain systems are vulnerable to cyberattacks due to asymmetric encryption and timestamp, which seriously impacts security and fairness. Therefore, based on the self-similarity of QUIC network traffic, this study proposes an anomaly detection model for QUIC network traffic based on Ensemble Empirical Mode Decomposition and Approximate Entropy. The model obtains several intrinsic mode functions and a residual term by decomposing the network traffic, introducing Approximate Entropy to judge the complexity of each component, and using the Hurst parameter to determine the generation of abnormal traffic. Compared with traditional detection methods, the model can effectively overcome the problems of poor adaptive ability and low efficiency and has high accuracy, which can be used for attack detection and prevention of network nodes. K E Y W O R D S computer network security, data flow analysis, software reliability, transport protocols 1 | INTRODUCTION Blockchain technology is an integrated and innovative application of various IT technologies. It is a technical solution for storing, verifying, transmitting, and exchanging network data without relying on a third party and its distributed nodes. It has two core characteristics: data is difficult to tamper with and decentralisation. Based on these two characteristics, the information recorded by the Blockchain is more authentic and reliable, which can help solve the problem of people's mutual distrust [1, 2]. It solves the problem of data trust and security through distributed ledgers, consensus mechanisms, smart contracts, authorisation, and encryption [3]. In recent years, Blockchain technology is continuously empowering various fields. Figure 1 shows a 5G wireless network and Blockchainbased network architecture. The architecture collects real-time data through the Internet of Things and uploads the data and events to the Blockchain platform, enabling secure scheduling of terminal devices connected to the 5G network [4]. Integrating 5G and Blockchain technology will open the era of inter-chain integration, which can provide an efficient, safe, and fast service experience. With the continuous development This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Eemd Algorithmmentioning

confidence: 99%

Retracted: Anomaly detection with ensemble empirical mode decomposition and approximate entropy for quick user datagram protocol internet connection‐based distributed Blockchain systems

Cao

et al. 2023

IET Software

Self Cite

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“…According to equation (9),The osprey carries a fish it has caught to a good location where it will consume it.…”

Section:  mentioning

confidence: 99%

Research on vehicle battery data cleaning method based on OOA-VMD-ATGRU-GAN

Ding,

Sun,

et al. 2024

Phys. Scr.

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Battery health monitoring is influenced by environmental and human factors, resulting in the presence of abnormal and missing values in the detection data. These issues compromise the accuracy of subsequent life prediction and fault diagnosis. To address this problem, we propose a deep learning-based method for cleaning battery anomalies and imputing missing data. Initially, we optimize the Variational Modal Decomposition method using the Osprey Optimization Algorithm to minimize the influence of continuous discharge processes on local anomaly detection. This process allows us to obtain the trend of the time series, and subsequently, we determine the anomalies by using the interquartile range after removing the trend components. The identified anomalies are then converted into missing values for further processing. Next, we fill in these missing values by constructing a Generative Adversarial Network. The generator structure of the network combines the attention mechanism with the Gated Recurrent Unit. We validate our approach using a real vehicle dataset and subsequently perform prediction on the cleaned dataset. Our results demonstrate that the subsequent prediction model improves the RMSE value by approximately 35% and the MAPE value by roughly 42%. Thus, our method effectively enhances the quality of the original data.

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“…However, the massive amounts of image information cause network congestion. Although some novel technologies of congestion control and packet reordering algorithms are proposed to solve this problem [5][6][7], the image compression technology in the image sensor device has attracted an increasing attention and is considered as an effective solution in terms of improving energy and transmission efficiency. Until now, many image compression algorithms for WSN have been proposed [8].…”

Section: Introductionmentioning

confidence: 99%

Image Compression for Wireless Sensor Network: A Model Segmentation-Based Compressive Autoencoder

Bao,

Ye,

Han

et al. 2023

Wireless Communications and Mobile Computing

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Aiming at the problems of image quality, compression performance, and transmission efficiency of image compression in wireless sensor networks (WSN), a model segmentation-based compressive autoencoder (MS-CAE) is proposed. In the proposed algorithm, we first divide each image in the dataset into pixel blocks and design a novel deep image compression network with a compressive autoencoder to form a compressed feature map by encoding pixel blocks. Then, the reconstructed image is obtained by using the quantized coefficients of the quantizer and splicing the decoded feature maps in order. Finally, the deep network model is segmented into two parts: the encoding network and the decoding network. The weight parameters of the encoding network are deployed to the edge device for the compressed image in the sensor network. For high-quality reconstructed images, the weight parameters of the decoding network are deployed to the cloud system. Experimental results demonstrate that the proposed MS-CAE obtains a high signal-to-noise ratio (PSNR) for the details of the image, and the compression ratio at the same bit per pixel (bpp) is significantly higher than that of the compared image compression algorithms. It also indicates that the MS-CAE not only greatly relieves the pressure of the hardware system in sensor network but also effectively improves image transmission efficiency and solves the deployment problem of image monitoring in remote and energy-poor areas.

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Empirical Mode Decomposition-empowered Network Traffic Anomaly Detection for Secure Multipath TCP Communications

Cited by 12 publications

References 30 publications

Retracted: Anomaly detection with ensemble empirical mode decomposition and approximate entropy for quick user datagram protocol internet connection‐based distributed Blockchain systems

Retracted: Anomaly detection with ensemble empirical mode decomposition and approximate entropy for quick user datagram protocol internet connection‐based distributed Blockchain systems

Research on vehicle battery data cleaning method based on OOA-VMD-ATGRU-GAN

Image Compression for Wireless Sensor Network: A Model Segmentation-Based Compressive Autoencoder

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