CN114157411B - 一种基于LeNet5-SVM的分组加密识别方法 - Google Patents
一种基于LeNet5-SVM的分组加密识别方法 Download PDFInfo
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- 238000013527 convolutional neural network Methods 0.000 abstract description 4
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0618—Block ciphers, i.e. encrypting groups of characters of a plain text message using fixed encryption transformation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
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- Computer Networks & Wireless Communication (AREA)
- General Health & Medical Sciences (AREA)
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- Computer Security & Cryptography (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
Description
分类 | LeNet5-SVM |
3DES,AES | 95.36% |
3DES,Blowfish | 97.12% |
AES,Blowfish | 96.57% |
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CN114157411A CN114157411A (zh) | 2022-03-08 |
CN114157411B true CN114157411B (zh) | 2024-04-05 |
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Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015102783A2 (en) * | 2013-11-29 | 2015-07-09 | Portland State University | Construction and uses of variable-input-length tweakable ciphers |
CN106452733A (zh) * | 2016-11-24 | 2017-02-22 | 中国电子科技集团公司第三十研究所 | 一种基于密文分析的分组密码识别方法 |
CN110110085A (zh) * | 2019-04-24 | 2019-08-09 | 中电海康集团有限公司 | 基于字符级神经网络与svm的交通事故文本分类方法和*** |
CN111460472A (zh) * | 2020-03-20 | 2020-07-28 | 西北大学 | 一种基于深度学习图网络的加密算法识别方法 |
CN112597519A (zh) * | 2020-12-28 | 2021-04-02 | 杭州电子科技大学 | Ofdm加密***中基于卷积神经网络的无密钥解密方法 |
CN112910853A (zh) * | 2021-01-18 | 2021-06-04 | 南京信息工程大学 | 基于混合特征的加密流量分类方法 |
CN113177218A (zh) * | 2021-05-12 | 2021-07-27 | 广东南方信息安全研究院 | 一种基于密文特征识别加密算法类型的方法与*** |
CN113468562A (zh) * | 2021-06-22 | 2021-10-01 | 西安理工大学 | 超混沌***与神经网络机制融合的图像块加密解密算法 |
-
2021
- 2021-11-29 CN CN202111430633.2A patent/CN114157411B/zh active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015102783A2 (en) * | 2013-11-29 | 2015-07-09 | Portland State University | Construction and uses of variable-input-length tweakable ciphers |
CN106452733A (zh) * | 2016-11-24 | 2017-02-22 | 中国电子科技集团公司第三十研究所 | 一种基于密文分析的分组密码识别方法 |
CN110110085A (zh) * | 2019-04-24 | 2019-08-09 | 中电海康集团有限公司 | 基于字符级神经网络与svm的交通事故文本分类方法和*** |
CN111460472A (zh) * | 2020-03-20 | 2020-07-28 | 西北大学 | 一种基于深度学习图网络的加密算法识别方法 |
CN112597519A (zh) * | 2020-12-28 | 2021-04-02 | 杭州电子科技大学 | Ofdm加密***中基于卷积神经网络的无密钥解密方法 |
CN112910853A (zh) * | 2021-01-18 | 2021-06-04 | 南京信息工程大学 | 基于混合特征的加密流量分类方法 |
CN113177218A (zh) * | 2021-05-12 | 2021-07-27 | 广东南方信息安全研究院 | 一种基于密文特征识别加密算法类型的方法与*** |
CN113468562A (zh) * | 2021-06-22 | 2021-10-01 | 西安理工大学 | 超混沌***与神经网络机制融合的图像块加密解密算法 |
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