Escape method of malicious traffic based on backdoor attack
Launching backdoor attacks against deep learning (DL)-based network traffic classifiers, and a method of malicious traffic escape was proposed based on the backdoor attack. Backdoors were embedded in classifiers by mixing poisoned training samples with clean samples during the training process. Thes...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024077/ |
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| _version_ | 1841539197023813632 |
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| author | MA Bowen GUO Yuanbo MA Jun ZHANG Qi FANG Chen |
| author_facet | MA Bowen GUO Yuanbo MA Jun ZHANG Qi FANG Chen |
| author_sort | MA Bowen |
| collection | DOAJ |
| description | Launching backdoor attacks against deep learning (DL)-based network traffic classifiers, and a method of malicious traffic escape was proposed based on the backdoor attack. Backdoors were embedded in classifiers by mixing poisoned training samples with clean samples during the training process. These backdoor classifiers then identified the malicious traffic with an attacker-specific backdoor trigger as benign, allowing the malicious traffic to escape. Additionally, backdoor classifiers behaved normally on clean samples, ensuring the backdoor's concealment. Different backdoor triggers were adopted to generate various backdoor models, the effects of different malicious traffic on different backdoor models were compared, and the influence of different backdoors on the model's performance was analyzed. The effectiveness of the proposed method was verified through experiments, providing a new approach for escaping malicious traffic from classifiers. |
| format | Article |
| id | doaj-art-5f7377671faf43b5bf8f6171ac017e0a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5f7377671faf43b5bf8f6171ac017e0a2025-01-14T07:24:11ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-04-0145738359254674Escape method of malicious traffic based on backdoor attackMA BowenGUO YuanboMA JunZHANG QiFANG ChenLaunching backdoor attacks against deep learning (DL)-based network traffic classifiers, and a method of malicious traffic escape was proposed based on the backdoor attack. Backdoors were embedded in classifiers by mixing poisoned training samples with clean samples during the training process. These backdoor classifiers then identified the malicious traffic with an attacker-specific backdoor trigger as benign, allowing the malicious traffic to escape. Additionally, backdoor classifiers behaved normally on clean samples, ensuring the backdoor's concealment. Different backdoor triggers were adopted to generate various backdoor models, the effects of different malicious traffic on different backdoor models were compared, and the influence of different backdoors on the model's performance was analyzed. The effectiveness of the proposed method was verified through experiments, providing a new approach for escaping malicious traffic from classifiers.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024077/backdoor attackescape of malicious trafficdeep learningnetwork traffic classification |
| spellingShingle | MA Bowen GUO Yuanbo MA Jun ZHANG Qi FANG Chen Escape method of malicious traffic based on backdoor attack Tongxin xuebao backdoor attack escape of malicious traffic deep learning network traffic classification |
| title | Escape method of malicious traffic based on backdoor attack |
| title_full | Escape method of malicious traffic based on backdoor attack |
| title_fullStr | Escape method of malicious traffic based on backdoor attack |
| title_full_unstemmed | Escape method of malicious traffic based on backdoor attack |
| title_short | Escape method of malicious traffic based on backdoor attack |
| title_sort | escape method of malicious traffic based on backdoor attack |
| topic | backdoor attack escape of malicious traffic deep learning network traffic classification |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024077/ |
| work_keys_str_mv | AT mabowen escapemethodofmalicioustrafficbasedonbackdoorattack AT guoyuanbo escapemethodofmalicioustrafficbasedonbackdoorattack AT majun escapemethodofmalicioustrafficbasedonbackdoorattack AT zhangqi escapemethodofmalicioustrafficbasedonbackdoorattack AT fangchen escapemethodofmalicioustrafficbasedonbackdoorattack |