Research on information steganography based on network data stream

Lu, Jiazhong; Zhang, Weisha; Deng, Ziye; Zhang, Shibin; Chang, Yan

doi:10.1007/s00521-020-05260-4

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…In our previous work [29][30][31][32], we used the spatiotemporal features of traffic for anomaly detection, such as temporal features, combined features, and protocol features. In our algorithm, we not only use the features proposed by our previous work (such as the average number of packets of upstream and downstream traffic, the total size of upstream and downstream data packets, traffic duration, etc.…”

Section: Extraction Of Traffic Spatiotemporal Featuresmentioning

confidence: 99%

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Lan

Huang

et al. 2023

Preprint

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Considering the robustness and accuracy of conventional intrusion detection models are easily influenced by adversarial attacks, this work proposes an anti-attack intrusion detection algorithm based on a message-passing neural network with traffic spatiotemporal features. Our algorithm can not only effectively distinguish and correlate upstream and downstream traffics, but also clearly embody the relationship between different traffics from the same source node and destination node by the established graph structure. We also improve the model by utilising the characteristics of the existing network attacks, which indicates thatthe traffic spatiotemporal characteristics could achieve high accuracy on attack traffic detection. Compared to the previous algorithms, extensive experiments show that our proposed model outperforms other similar models in performance. For the multi-classification tasks, our algorithm can effectively detect most of the network attacks and outperform traditional machine learning methods. In terms of algorithm robustness, our algorithm is less affected by adversarial attacks than traditional machine learning algorithms.

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Section: Extraction Of Traffic Spatiotemporal Featuresmentioning

confidence: 99%

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Lan

Huang

et al. 2023

Preprint

Self Cite

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“…Therefore, a meaningful secret image sharing scheme (MSISS), also called the extended secret image sharing scheme, was proposed by Ateniese et al [10]. A noiselike shadow image can be embedded into meaningful carrier images by steganography, which is not bound by the sharing principle and can be applied to various secret sharing schemes [11,12]. Wu et al [13] designed an invertible secret image-sharing scheme,which used steganography and realized authentication.…”

Section: Introductionmentioning

confidence: 99%

Authenticable medical image-sharing scheme based on embedded small shadow QR code and blockchain framework（China MM）

Wen

Jian

Fang

et al. 2022

Preprint

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In the field of medical image content protection and security sharing, the introduction of blockchain technology has a problem in that the secret key may be lost and cannot be recovered. Therefore, this paper proposes an authenticable medical image-sharing scheme based on an embedded small shadow QR code and a blockchain framework. First, a small shadow image is obtained by employing a secret image-sharing method based on the Chinese remainder theorem. Then, the shadow image is embedded into a QR code with error correction. This not only ensures the security and integrity of the shadow image transmission in the public channel but also avoids the problem where the secret key is lost and cannot be recovered. Compared with some existing excellent schemes, our scheme adopts a hash bit stream for authentication purposes and employs smart contracts to authenticate and restore secret images, thus reducing the local load. In addition, experiments validate that the recovered secret medical image is lossless and that the cover QR code is more robust and secure. This method is suitable for medical image content protection and security sharing.

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“…Chen and Huang [18] compares the weight adjustment method with BP neural network and other methods. Liu et al [19] proposes a new type of data steganography technology based on network data flow. Lu and Zhang [20] improves the traditional data mining methods to different degrees according to the characteristics of different detection objects and puts forward some new detection data analysis and processing and fault diagnosis and prediction methods.…”

mentioning

confidence: 99%

Editorial: Special issue on neural computing and applications in cyber intelligence: ATCI 2020

Abawajy

2021

Neural Comput & Applic

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Research on information steganography based on network data stream

Cited by 9 publications

References 18 publications

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Authenticable medical image-sharing scheme based on embedded small shadow QR code and blockchain framework（China MM）

Editorial: Special issue on neural computing and applications in cyber intelligence: ATCI 2020

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