Method to generate cyber deception traffic based on adversarial sample
In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2020-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020166/ |
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| _version_ | 1841539264129531904 |
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| author | Yongjin HU Yuanbo GUO Jun MA Han ZHANG Xiuqing MAO |
| author_facet | Yongjin HU Yuanbo GUO Jun MA Han ZHANG Xiuqing MAO |
| author_sort | Yongjin HU |
| collection | DOAJ |
| description | In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception. |
| format | Article |
| id | doaj-art-24d087a65f1440fa95906075e4b7dbad |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-24d087a65f1440fa95906075e4b7dbad2025-01-14T07:19:50ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-09-0141597059737208Method to generate cyber deception traffic based on adversarial sampleYongjin HUYuanbo GUOJun MAHan ZHANGXiuqing MAOIn order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020166/adversarial samplenetwork traffic classificationcyber deceptionnetwork traffic obfuscationdeep learning |
| spellingShingle | Yongjin HU Yuanbo GUO Jun MA Han ZHANG Xiuqing MAO Method to generate cyber deception traffic based on adversarial sample Tongxin xuebao adversarial sample network traffic classification cyber deception network traffic obfuscation deep learning |
| title | Method to generate cyber deception traffic based on adversarial sample |
| title_full | Method to generate cyber deception traffic based on adversarial sample |
| title_fullStr | Method to generate cyber deception traffic based on adversarial sample |
| title_full_unstemmed | Method to generate cyber deception traffic based on adversarial sample |
| title_short | Method to generate cyber deception traffic based on adversarial sample |
| title_sort | method to generate cyber deception traffic based on adversarial sample |
| topic | adversarial sample network traffic classification cyber deception network traffic obfuscation deep learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020166/ |
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