Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD
The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the univers...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/9/3/177 |
| _version_ | 1797443518203953152 |
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| author | Hongwei Du Duan Huang |
| author_facet | Hongwei Du Duan Huang |
| author_sort | Hongwei Du |
| collection | DOAJ |
| description | The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the universality of detection; however, current universal detection methods only consider the independent existence of attacks but ignore the possible coexistence of multiple attacks in reality. Here, we propose two multi-attack neural network detection models to handle the coexistence of multiple attacks. The models adopt two methods in multi-label learning: binary relevance (BR) and label power (LP) to deal with the coexistence of multiple attacks and can identify attacks in real-time by autonomously learning the features of known attacks in a deep neural network. Further, we improve the model to detect unknown attacks simultaneously. The experimental results show that the proposed scheme can achieve high-precision detection for most known and unknown attacks without reducing the key rate and maximum transmission distance. |
| first_indexed | 2024-03-09T12:57:14Z |
| format | Article |
| id | doaj.art-c0012964927b40fd94c07d7a9a0285b8 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-09T12:57:14Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-c0012964927b40fd94c07d7a9a0285b82023-11-30T21:59:17ZengMDPI AGPhotonics2304-67322022-03-019317710.3390/photonics9030177Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKDHongwei Du0Duan Huang1School of Computer and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer and Engineering, Central South University, Changsha 410083, ChinaThe security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the universality of detection; however, current universal detection methods only consider the independent existence of attacks but ignore the possible coexistence of multiple attacks in reality. Here, we propose two multi-attack neural network detection models to handle the coexistence of multiple attacks. The models adopt two methods in multi-label learning: binary relevance (BR) and label power (LP) to deal with the coexistence of multiple attacks and can identify attacks in real-time by autonomously learning the features of known attacks in a deep neural network. Further, we improve the model to detect unknown attacks simultaneously. The experimental results show that the proposed scheme can achieve high-precision detection for most known and unknown attacks without reducing the key rate and maximum transmission distance.https://www.mdpi.com/2304-6732/9/3/177CVQKDmulti-label learningGRUone-class SVM |
| spellingShingle | Hongwei Du Duan Huang Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD Photonics CVQKD multi-label learning GRU one-class SVM |
| title | Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD |
| title_full | Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD |
| title_fullStr | Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD |
| title_full_unstemmed | Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD |
| title_short | Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD |
| title_sort | multi attack detection general defense strategy based on neural networks for cv qkd |
| topic | CVQKD multi-label learning GRU one-class SVM |
| url | https://www.mdpi.com/2304-6732/9/3/177 |
| work_keys_str_mv | AT hongweidu multiattackdetectiongeneraldefensestrategybasedonneuralnetworksforcvqkd AT duanhuang multiattackdetectiongeneraldefensestrategybasedonneuralnetworksforcvqkd |