Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Zhou, Xiaokang; Liang, Wei; Shimizu, Shohei; Ma, Jun; Jin, Qun

doi:10.1109/tii.2020.3047675

Cited by 203 publications

(64 citation statements)

References 23 publications

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“…And thinking about the case that there are many 0s in , we add a dropout layer after LSTM layer. Per relevant literatures [18,[30][31][32][33][34][35][36] and our verification, two LSTM layers are sufficient in learning time series relationship. Thus, we use two LSTM layers in both generator and discriminator.…”

Section: Generator Of Lstm-cgan Modelmentioning

confidence: 51%

LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models

Liu

Yin

2021

IEEE Access

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Low-rate Distributed DoS (LDDoS) attack is a complex large-scale attack behavior with strong time-domain characteristics in blockchain-based wireless network. Blockchain with Machine learning-based models, as promising ways, are taken to detect them and secure wireless network. However, researchers focused on how to improve models' detection performance and work out new blockchain-based protection technologies during the past decades. Due to lack of evolving data, these models and technologies may have poor stability in the face of confrontational samples. To cope with the problem, this paper proposes a novel LSTM-CGAN method to generate high-quality LDDoS adversarial samples for blockchain-based wireless network detection models. In this method, we give a brief feature analysis about LDDoS attack in blockchainbased wireless network and work out its corresponding time series model firstly. And then, we take use of Long Short-Term Memory Networks (LSTM) to learn relationships among sequenced network packages in the same flow. At last, we establish a Condition Generative Adversarial Networks (CGAN) model to use above relationships as specific conditions for generating mimicking behaviors of LDDoS attacks in blockchain-based wireless network. The experimental results show that these generated adversarial samples based on both public and private datasets can cheat the machine learning detection models, and have the similar attack characteristics with the real samples. Consequently, they can be used as blockchain-based wireless network dataset of machine learning classifiers for training to enhance models' stability.

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Section: Generator Of Lstm-cgan Modelmentioning

confidence: 51%

LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models

Liu

Yin

2021

IEEE Access

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“…In addition, transparent player transfer with privacy protection can be regarded as a secure multi-party computation issue, i.e., secure similarity calculation with privacy-preservation. Therefore, we summarize the related work in the field from the following two perspectives: similarity-based matching [21][22][23][24][25][26] and collaboration with privacy-preservation.…”

Section: Related Workmentioning

confidence: 99%

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

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“…To solve the above problems, this paper proposes a realistic medical image SR method based on a pyramidal feature multiple distillations network [ 29 – 31 ]. RMISR aims at reconstructing SR images with clear visual perception and real high-frequency detail information.…”

Section: Related Workmentioning

confidence: 99%

Realistic medical image super-resolution with pyramidal feature multi-distillation networks for intelligent healthcare systems

Ren

Guo

Ma³

et al. 2021

Neural Comput & Applic

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There are two key requirements for medical lesion image super-resolution reconstruction in intelligent healthcare systems: clarity and reality. Because only clear and real super-resolution medical images can effectively help doctors observe the lesions of the disease. The existing super-resolution methods based on pixel space optimization often lack high-frequency details which result in blurred detail features and unclear visual perception. Also, the super-resolution methods based on feature space optimization usually have artifacts or structural deformation in the generated image. This paper proposes a novel pyramidal feature multi-distillation network for super-resolution reconstruction of medical images in intelligent healthcare systems. Firstly, we design a multi-distillation block that combines pyramidal convolution and shallow residual block. Secondly, we construct a two-branch super-resolution network to optimize the visual perception quality of the super-resolution branch by fusing the information of the gradient map branch. Finally, we combine contextual loss and L1 loss in the gradient map branch to optimize the quality of visual perception and design the information entropy contrast-aware channel attention to give different weights to the feature map. Besides, we use an arbitrary scale upsampler to achieve super-resolution reconstruction at any scale factor. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance compared to other methods in this work.

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Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Cited by 203 publications

References 23 publications

LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models

LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Realistic medical image super-resolution with pyramidal feature multi-distillation networks for intelligent healthcare systems

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