Journal on Communications ›› 2020, Vol. 41 ›› Issue (9): 59-70.doi: 10.11959/j.issn.1000-436x.2020166
• Papers • Previous Articles Next Articles
Yongjin HU1,Yuanbo GUO1,Jun MA1,Han ZHANG1,2(),Xiuqing MAO1
Revised:
2020-06-16
Online:
2020-09-25
Published:
2020-10-12
Supported by:
CLC Number:
Yongjin HU,Yuanbo GUO,Jun MA,Han ZHANG,Xiuqing MAO. Method to generate cyber deception traffic based on adversarial sample[J]. Journal on Communications, 2020, 41(9): 59-70.
"
应用类别 | flow记录数量/条 | 所占比例 | 对应标签 |
Web | 328 092 | 86.906% | 0 |
28 567 | 7.567% | 1 | |
FTP-control | 3 054 | 0.809% | 2 |
FTP-pasv | 2 688 | 0.712% | 3 |
attack | 1 793 | 0.475% | 4 |
P2P | 2 094 | 0.555% | 5 |
database | 2 648 | 0.701% | 6 |
FTP-data | 5 797 | 1.536% | 7 |
multimedia | 5 76 | 0.153% | 8 |
services | 2 099 | 0.556% | 9 |
interactive | 110 | 0.028% | 10 |
games | 8 | 0.002% | 11 |
合计 | 377 526 | 100% | 12 |
"
流量应用 | 单类欺骗率 | 分类分布 | |||||||
方法 | 欺骗率 | Web | FTP-control | FTP-pasv | attack | 其他 | |||
FGSM | 96% | 4% | 22% | 0 | 0 | 0 | 74% | ||
Web | DeepFool | 98% | 2% | 32% | 1% | 0 | 0 | 64% | |
C&W | 73% | 27% | 16% | 0 | 0 | 0 | 56% | ||
FGSM | 96% | 1% | 4% | 0 | 0 | 0 | 95% | ||
DeepFool | 96% | 14% | 4% | 0 | 0 | 0 | 82% | ||
C&W | 68% | 0 | 32% | 0 | 0 | 0 | 68% | ||
FGSM | 98% | 0 | 0 | 2% | 46% | 0 | 51% | ||
FTP-control | DeepFool | 94% | 0 | 0 | 6% | 91% | 0 | 3% | |
C&W | 61% | 0 | 0 | 39% | 61% | 0 | 0 | ||
FGSM | 94% | 0 | 0 | 73% | 6% | 0 | 21% | ||
FTP-pasv | DeepFool | 90% | 0 | 0 | 88% | 10% | 0 | 2% | |
C&W | 74% | 0 | 0 | 73% | 26% | 0 | 1% | ||
FGSM | 98% | 21% | 12% | 0 | 0 | 2% | 65% | ||
attack | DeepFool | 98% | 1% | 0 | 0 | 0 | 2% | 97% | |
C&W | 68% | 44% | 0 | 0 | 0 | 32% | 24% |
[1] | FRANK J . Artificial intelligence and intrusion detection:current and future directions[J]. Computers & Security, 1995,14(1):31. |
[2] | SZEGEDY C , ZAREMBA W , SUTSKEVER I ,et al. Intriguing properties of neural networks[J]. arXiv Preprint,arXiv:1312.6199, 2013 |
[3] | SITAWARIN C , BHAGOJI AN , MOSENIA A ,et al. Rogue signs:deceiving traffic sign recognition with malicious ads and logos[J]. arXiv Preprint,arXiv:1801.02780, 2018 |
[4] | YANG K , LIU J , ZHANG C ,et al. Adversarial examples against the deep learning based network intrusion detection system[C]// 2018 IEEE Military Communications Conference. Piscataway:IEEE Press, 2018: 559-564. |
[5] | HU W W , TAN Y . Generating adversarial malware examples for black-box attacks based on GAN[J]. arXiv Preprint,arXiv:1702.05983, 2017 |
[6] | 熊刚, 孟姣, 曹自刚 ,等. 网络流量分类研究进展与展望[J]. 集成技术, 2012,1(1): 32-42. |
XIONG G , MENG J , CAO Z G ,et al. Research progress and prospects of network traffic classification[J]. Journal of Integration Technology, 2012,1(1): 32-42. | |
[7] | WANG Z . The applications of deep learning on tracidentification[J]. BlackHat USA, 2015,24(11): 21-26. |
[8] | WANG W , ZHU M , WANG J ,et al. End-to-end encrypted traffic classification with one-dimensional convolution neural networks[C]// 2017 IEEE International Conference on Intelligence and Security Informatics. Piscataway:IEEE Press, 2017: 43-48. |
[9] | RIMMER V , PREUVENEERS D , JUAREZ M ,et al. Automated website fingerprinting through deep learning[J]. arXiv Preprint,arXiv:1708.06376, 2017 |
[10] | WANG W , ZHU M , ZENG X ,et al. Malware traffic classification using convolutional neural network for representation learning[C]// 2017 International Conference on Information Networking. Piscataway:IEEE Press, 2017: 712-717. |
[11] | PANCHENKO A , NIESSEN L , ZINNEN A ,et al. Website fingerprinting in onion routing based anonymization networks[C]// Proceedings of 27 the 10th annual ACM workshop on Privacy in the electronic society. New York:ACM Press, 2011: 103-114. |
[12] | DYER K P , COULL S E , RISTENPART T ,et al. Peek-a-boo,i still see you:why efficient traffic analysis countermeasures fail[C]// 2012 IEEE Symposium on Security and Privacy. Piscataway:IEEE Press, 2012: 332-346. |
[13] | CUI W , YU J , GONG Y ,et al. Realistic cover traffic to mitigate website fingerprinting attacks[C]// 2018 IEEE 38th International Conference on Distributed Computing Systems. Piscataway:IEEE Press, 2018: 1579-1584. |
[14] | WANG T , GOLDBERG I . Walkie-talkie:an efficient defense against passive website fingerprinting attacks[C]// Proceedings of the 26th USENIX Security Symposium. Berkeley:USENIX Association, 2017: 1375-1390. |
[15] | DINGLEDINE R . Obfsproxy:the next step in the censorship arms race[R]. TOR Project official,(2012-05-23)[2020-03-20]. |
[16] | MOGHADDAM H , LI B , DERAKHSHANI M ,et al. SkypeMorph:protocol obfuscation for TOR bridges[C]// Proceedings of the 2012 ACM Conference on Computer and Communications Security. New York:ACM Press, 2012: 97-108. |
[17] | LI F F , KAKHKI A M , CHOFFNES D ,et al. Classifiers unclassified:an efficient approach to revealing IP traffic classification rules[C]// Proceedings of the 2016 Internet Measurement Conference. New York:ACM Press, 2016: 239-245. |
[18] | 张思思, 左信, 刘建伟 . 深度学习中的对抗样本问题[J]. 计算机学报, 2019,42(8): 1886-1904. |
ZHANG S S , ZUO X , LIU J W . The problem of the adversarial exam-ples in deep learning[J]. Chinese Journal of Computers, 2019,42(8): 1886-1904. | |
[19] | GOODFELLOW I , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples[J]. arXiv Preprint,arXiv:1412.6572, 2014 |
[20] | KURAKIN A , GOODFELLOW I , BENGIO S . Adversarial examples in the physical world[J]. arXiv Preprint,arXiv:1607.02533, 2016 |
[21] | MOOSAVI-DEZFOOLI S M , FAWZI A , FAWZI O ,et al. Universal Adversarial Perturbations[C]// The IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 1765-1773. |
[22] | ATHALYE A , ENGSTROM L , ILYAS A ,et al. Synthesizing robust adversarial examples[J]. arXiv Preprint,arXiv:1707.07397, 2017 |
[23] | MOOSAVI-DEZFOOLI S M , FAWZI A , FROSSARD P . DeepFool:a simple and accurate method to fool deep neural networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 2574-2582. |
[24] | METZEN J H , KUMAR M C , BROX T ,et al. Universal adversarial perturbations against semantic image segmentation[C]// The IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017: 2755-2764. |
[25] | MOPURI K R , GARG U , BAHU R V . Fast feature fool:a data independent approach to universal adversarial perturbations[J]. arXiv Preprint,arXiv:1707.05572, 2017 |
[26] | MOPURI K R , GANESHAN A , BABU R V . Generalizable data-free objective for crafting universal adversarial perturbations[C]// IEEE Transactions on Pattern Analysis and Machine Intelligence. Piscataway:IEEE Press, 2019,41(10): 2452-2465. |
[27] | VERMA G , CIFTCIOGLU E , SHEATSLEY R ,et al. Network traffic obfuscation:an adversarial machine learning approach[C]// 2018 IEEE Military Communications Conference. Piscataway:IEEE Press, 2018: 1-6. |
[28] | 潘文雯, 王新宇, 宋明黎 ,等. 对抗样本生成技术综述[J]. 软件学报, 2020,31(1): 67-81. |
PAN W W , WANG X Y , SONG M L ,et al. Survey on generating ad-versarial examples[J]. Journal of Software, 2020,31(1): 67-81. | |
[29] | CARLINI N , WAGNER D . Towards evaluating the robustness of neural networks[C]// 2017 IEEE Symposium on Security and Privacy. Piscataway:IEEE Press, 2017: 39-57. |
[30] | HE K M , SUN J . Convolutional neural networks at constrained time cost[C]// The IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2015: 5353-5360. |
[31] | MOORE A W , ZUEV D . Discriminators for use in flow-based classification[R]. Intel Research,Cambridge,(2005-08)[2020-03-19]. |
[32] | LENCUN Y , BOTTOU L , BENGIO Y . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,862: 2278-2324 |
[33] | 王勇, 周慧怡, 俸皓 ,等. 基于深度卷积神经网络的网络流量分类方法[J]. 通信学报, 2018,39(1): 14-23. |
WANG Y , ZHOU H Y , FENG H ,et al. Network traffic classification method basing on CNN[J]. Journal on Communications, 2018,39(1): 14-23. |
[1] | Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG. Research on test strategy for randomness based on deep learning [J]. Journal on Communications, 2023, 44(6): 23-33. |
[2] | Rongpeng LI, Bingyan WANG, Honggang ZHANG, Zhifeng ZHAO. Design of knowledge enhanced semantic communication receiver [J]. Journal on Communications, 2023, 44(6): 70-76. |
[3] | Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI. Research on geomagnetic indoor high-precision positioning algorithm based on generative model [J]. Journal on Communications, 2023, 44(6): 211-222. |
[4] | Yifeng WANG, Yuanbo GUO, Qingli CHEN, Chen FANG, Renhao LIN, Yongliang ZHOU, Jiali MA. Method based on contrastive incremental learning for fine-grained malicious traffic classification [J]. Journal on Communications, 2023, 44(3): 1-11. |
[5] | Chengsheng YUAN, Qiang GUO, Zhangjie FU. Copyright protection algorithm based on differential privacy deep fake fingerprint detection model [J]. Journal on Communications, 2022, 43(9): 181-193. |
[6] | Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI. Lightweight decentralized learning-based automatic modulation classification method [J]. Journal on Communications, 2022, 43(7): 134-142. |
[7] | Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI. Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF [J]. Journal on Communications, 2022, 43(6): 58-70. |
[8] | Yong LIAO, Shiyi WANG. CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment [J]. Journal on Communications, 2022, 43(5): 166-176. |
[9] | Yurong LIAO, Haining WANG, Cunbao LIN, Yang LI, Yuqiang FANG, Shuyan NI. Research progress of deep learning-based object detection of optical remote sensing image [J]. Journal on Communications, 2022, 43(5): 190-203. |
[10] | Zenghua ZHAO, Yuefan TONG, Jiayang CUI. Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation [J]. Journal on Communications, 2022, 43(4): 143-153. |
[11] | Yong LIAO, Gang CHENG, Yujie LI. CSI feedback algorithm based on deep unfolding for massive MIMO systems [J]. Journal on Communications, 2022, 43(12): 77-88. |
[12] | Xueyuan DUAN, Yu FU, Kun WANG, Bin LI. LDoS attack detection method based on simple statistical features [J]. Journal on Communications, 2022, 43(11): 53-64. |
[13] | Junyan HUO, Ruipeng QIU, Yanzhuo MA, Fuzheng YANG. Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture [J]. Journal on Communications, 2022, 43(11): 136-147. |
[14] | Haiyan KANG, Yuanrui JI. Research on federated learning approach based on local differential privacy [J]. Journal on Communications, 2022, 43(10): 94-105. |
[15] | Hongxia ZHANG, Qi WANG, Dengyue WANG, Ben WANG. Honeypot contract detection of blockchain based on deep learning [J]. Journal on Communications, 2022, 43(1): 194-202. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|