Journal on Communications ›› 2020, Vol. 41 ›› Issue (6): 128-138.doi: 10.11959/j.issn.1000-436x.2020122
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Tieming CHEN,Chengqiang JIN,Mingqi LYU,Tiantian ZHU
Revised:
2019-12-18
Online:
2020-06-25
Published:
2020-07-04
Supported by:
CLC Number:
Tieming CHEN,Chengqiang JIN,Mingqi LYU,Tiantian ZHU. Intelligent detection method on network malicious traffic based on sample enhancement[J]. Journal on Communications, 2020, 41(6): 128-138.
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加噪比例 | TPR | FPR | 精确度 | 召回率 | F-measure | 准确率 |
0 | 94.56% | 48.09% | 66.31% | 94.56% | 77.95% | 73.24% |
5% | 94.64% | 20.67% | 82.07% | 94.64% | 87.91% | 86.98% |
10% | 95.63% | 17.19% | 84.75% | 95.63% | 89.86% | 89.22% |
15% | 80.15% | 16.02% | 83.34% | 80.15% | 81.71% | 82.06% |
20% | 16.71% | 15.86% | 51.27% | 16.71% | 25.21% | 50.44% |
30% | 68.62% | 20.60% | 76.89% | 68.62% | 72.52% | 74.01% |
40% | 51.91% | 5.47% | 90.45% | 51.91% | 65.96% | 73.23% |
50% | 67.20% | 13.28% | 83.49% | 67.20% | 74.46% | 76.97% |
[1] | 谢逸, 余顺争 . 基于Web用户浏览行为的统计异常检测[J]. 软件学报, 2007,18(4): 967-977. |
XIE Y , YU S Z . Anomaly detection based on Web users' browsing behaviors[J]. Journal of Software, 2007,18(4): 967-977. | |
[2] | ZHANG X , ZHAO J , LECUN Y . Character-level convolutional networks for text classification[C]// Advances in neural information processing systems. Massachusetts:MIT Press, 2015: 649-657. |
[3] | LU X , ZHENG B , VELIVELLI A ,et al. Enhancing text categorization with semantic-enriched representation and training data augmentation[J]. Journal of the American Medical Informatics Association, 2006,13(5): 526-535. |
[4] | ZOLOTUKHIN M , H?M?L?INEN T , KOKKONEN T , .et al. Analysis of http requests for anomaly detection of Web attacks[C]// 2014 IEEE 12th International Conference on Dependable,Autonomic and Secure Computing. Piscataway:IEEE Press, 2014: 406-411. |
[5] | PARK S , KIM M , LEE S . Anomaly detection for HTTP using convolutional autoencoders[J]. IEEE Access, 2018,6: 70884-70901. |
[6] | YU Y , LIU G , YAN H ,et al. Attention-based Bi-LSTM model for anomalous HTTP traffic detection[C]// 2018 15th International Conference on Service Systems and Service Management. Piscataway:IEEE Press, 2018: 1-6. |
[7] | YANG W , ZUO W , CUI B . Detecting malicious URLS via a keyword-based convolutional gated-recurrent-unit neural network[J]. IEEE Access, 2019,7: 29891-29900. |
[8] | ARZHAKOV A V , TROITSKIY S S , VASILYEV N P ,et al. Development and implementation a method of detecting an attacker with use of HTTP network protocol[C]// 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. Piscataway:IEEE Press, 2017: 100-104. |
[9] | XU F , PAN H , CAO Z ,et al. Identifying malware with HTTP content type inconsistency via header-payload comparison[C]// 2017 IEEE 36th International Performance Computing and Communications Conference. Piscataway:IEEE Press,, 2017: 1-7. |
[10] | TORRANO-GIMéNEZ C , PEREZ-VILLEGAS A , ALVAREZ MARANóN G . An anomaly-based approach for intrusion detection in Web traffic[J]. Journal of Information Assurance Security, 2010,5(4): 446-454. |
[11] | TAX D M J , DUIN R P W . Support vector data description[J]. Machine learning, 2004,54(1): 45-66. |
[12] | THANG T M , KIM J . The anomaly detection by using DBSCAN clustering with multiple parameters[C]// 2011 International Conference on Information Science and Applications. Piscataway:IEEE Press, 2011: 1-5. |
[13] | CHORA? M KOZIK R . Machine learning techniques applied to detect cyber attacks on Web applications[J]. Logic Journal of the IGPL, 2015,23(1): 45-56. |
[14] | KRUEGEL C , VIGNA G . Anomaly detection of Web-based attacks[C]// Proceedings of the 10th ACM conference on Computer and communications security. New York:ACM Press, 2003: 251-261. |
[15] | CORONA I , TRONCI R , GIACINTO G . SuStorID:a multiple classifier system for the protection of Web services[C]// Proceedings of the 21st International Conference on Pattern Recognition. Piscataway:IEEE Press, 2012: 2375-2378. |
[16] | RINGBERG H , SOULE A , REXFORD J ,et al. Sensitivity of PCA for traffic anomaly detection[C]// ACM SIGMETRICS Performance Evaluation Review. New York:ACM Press, 2007,35(1): 109-120. |
[17] | AL-OBEIDAT F , EL-ALFY E S M . Hybrid multicriteria fuzzy classification of network traffic patterns,anomalies,and protocols[J]. Personal and Ubiquitous Computing, 2019,23(5-6): 777-791. |
[18] | ERFANI S M , RAJASEGARAR S , KARUNASEKERA S ,et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J]. Pattern Recognition, 2016,58: 121-134. |
[19] | DU M , LI F , ZHENG G ,et al. Deeplog:anomaly detection and diagnosis from system logs through deep learning[C]// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York:ACM Press, 2017: 1285-1298. |
[20] | ZHANG M , LU S , XU B . An anomaly detection method based on multi-models to detect Web attacks[C]// 2017 10th International Symposium on Computational Intelligence and Design. Piscataway:IEEE Press, 20172: 404-409. |
[21] | CRETU-CIOCARLIE G F , STAVROU A , LOCASTO M E ,et al. Adaptive anomaly detection via self-calibration and dynamic updating[C]// International Workshop on Recent Advances in Intrusion Detection. Berlin:Springer, 2009: 41-60. |
[22] | WHITESON S , TANNER B , TAYLOR M E ,et al. Protecting against evaluation overfitting in empirical reinforcement learning[C]// 2011 IEEE symposium on adaptive dynamic programming and reinforcement learning. Piscataway:IEEE Press, 2011: 120-127. |
[23] | JIN Y , XIE J , GUO W ,et al. LSTM-CRF Neural Network With gated self-attention for Chinese NER[J]. IEEE Access, 2019,7: 136694-136703. |
[24] | KRIZHEVSKY A , SUTSKEVER I , HINTON G E . Imagenet classification with deep convolutional neural networks[C]// Advances in neural information processing systems. Massachusetts:MIT Press, 2012: 1097-1105. |
[25] | KIM Y . Convolutional neural networks for sentence classification[J]. arXiv preprint arXiv:1408.5882, 2014: 1746-1751. |
[26] | LECUN Y , BOTTOU L , BENGIO Y ,et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,86(11): 2278-2324. |
[27] | HAN J , KAMBER M . Data mining:concepts and techniques[M]. Berlin: Morgan Kaufmann PublishersPress, 2000. |
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