Telecommunications Science ›› 2023, Vol. 39 ›› Issue (7): 80-89.doi: 10.11959/j.issn.1000-0801.2023138
• Research and Development • Previous Articles Next Articles
Bensheng YUN, Xiaoya GAN, Yaguan QIAN
Revised:
2023-07-02
Online:
2023-07-20
Published:
2023-07-01
Supported by:
CLC Number:
Bensheng YUN, Xiaoya GAN, Yaguan QIAN. A network traffic classification method based on random forest and improved convolutional neural network[J]. Telecommunications Science, 2023, 39(7): 80-89.
"
对比项 | sparkmaster | sparkslaver 1 | sparkslaver 2 |
Spark | Master | Worker | Worker |
HDFS | NameNode DataNode | DataNode | SecondaryNameNode DataNode |
YARN | NodeManager | ResourceManager NodeManager | NodeManager |
注:Master 为 Spark 主节点,Worker 为 Spark 工作节点;NameNode 为 HDFS 管理节点,DataNode 为 HDFS 工作节点, SecondaryNameNode为辅助管理节点;ResourceManager为YARN管理节点,NodeManager为YARN工作节点。 |
"
特征选取方法 | 准确率 | 精确率 | 召回率 | F1值 | 训练时间/min |
原始字节前1 024个特征[ | 90.42% | 89.47% | 91.62% | 90.53% | — |
随机森林选择1 024个特征 | 97.68% | 97.94% | 97.42% | 97.68% | 97 |
随机森林选择400个特征 | 95.84% | 94.95% | 96.83% | 95.88% | 56 |
随机森林选择256个特征 | 95.03% | 92.35% | 98.20% | 95.19% | 52 |
随机森林选择100个特征 | 94.22% | 94.19% | 94.25% | 94.22% | 48 |
随机森林选择64个特征 | 87.20% | 92.27% | 81.20% | 86.38% | 26 |
[10] | MARí G , CAASAS P , CAPDEHOURAT G . DeepMAL - deep learning models for malware traffic detection and classification[C]// Data Science – Analytics and Applications. Wiesbaden:Springer Vieweg, 2021: 105-112. |
[11] | REIS B , MAIA E , PRA?A I . Selection and performance analysis of CICIDS2017 features importance[C]// International Symposium on Foundations and Practice of Security. Cham:Springer, 2020: 56-71. |
[12] | BREIMAN L . Random forests[J]. Machine Learning, 2001,45(1): 5-32. |
[13] | 陈卓, 吕娜 . 基于随机森林和XGBoost的网络入侵检测模型[J]. 信号处理, 2020,36(7): 1055-1064. |
CHEN Z , LYU N . Network intrusion detection model based on random forest and XGBoost[J]. Journal of Signal Processing, 2020,36(7): 1055-1064. | |
[14] | HE K M , ZHANG X Y , REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2016: 770-778. |
[15] | 甘众远 . 基于深度学习的轻量化恶意流量识别及其分布式方法的研究与实现[D]. 南京:南京邮电大学, 2021. |
GAN Z Y . Research and implementation of lightweight malicious traffic identification and its distributed method based on deep learning[D]. Nanjing:Nanjing University of Posts and Telecommunications, 2021. | |
[16] | LOSHCHILOV I , HUTTER F . Decoupled weight decay regularization[J]. arXiv preprint, 2017,arXiv:1711.05101. |
[17] | KINGMA D P , BA J . Adam:a method for stochastic optimization[J]. arXiv preprint, 2014,arXiv:1412.6980. |
[18] | 刘云飞, 张俊然 . 深度神经网络学习率策略研究进展[J]. 控制与决策, 2022:0147. |
LIU Y F , ZHANG J R . Research advances in deep neural networks learning rate strategies[J]. Control and Decision, 2022:0147. | |
[1] | 顾玥, 李丹, 高凯辉 . 基于机器学习和深度学习的网络流量分类研究[J]. 电信科学, 2021,37(3): 105-113. |
GU Y , LI D , GAO K H . Research on network traffic classification based on machine learning and deep learning[J]. Telecommunications Science, 2021,37(3): 105-113. | |
[2] | 冯文博, 洪征, 吴礼发 ,等. 网络协议识别技术综述[J]. 计算机应用, 2019,39(12): 3604-3614. |
FENG W B , HONG Z , WU L F ,et al. Review of network protocol recognition techniques[J]. Journal of Computer Applications, 2019,39(12): 3604-3614. | |
[3] | WANG W , ZHU M , ZENG X W ,et al. Malware traffic classification using convolutional neural network for representation learning[C]// Proceedings of 2017 International Conference on Information Networking (ICOIN). Piscataway:IEEE Press, 2017: 712-717. |
[4] | FENG W B , HONG Z , WU L F ,et al. Network protocol recognition based on convolutional neural network[J]. China Communications, 2020,17(4): 125-139. |
[5] | SUN Y L , YUN B S , QIAN Y G ,et al. A Spark-based method for identifying large-scale network burst traffic[J]. Journal of Computers, 2021,32(4): 123-136. |
[6] | TONG V , TRAN H A , SOUIHI S ,et al. A novel QUIC traffic classifier based on convolutional neural networks[C]// Proceedings of 2018 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2019: 1-6. |
[7] | HU X Y , GU C X , WEI F S . CLD-net:a network combining CNN and LSTM for Internet encrypted traffic classification[J]. Security and Communication Networks, 2021: 1-15. |
[8] | 于帅, 董育宁, 邱晓晖 . 一种基于深度特征融合的网络流量分类方法[J]. 南京邮电大学学报(自然科学版), 2022,42(3): 82-89. |
YU S , DONG Y N , QIU X H . A network traffic classification method based on deep feature fusion[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science), 2022,42(3): 82-89. | |
[9] | 薛靖靓, 陈迎春, 李鸥 . 未知流量数据的智能特征提取与实时分类识别算法[J]. 信息工程大学学报, 2021,22(5): 597-605. |
XUE J L , CHEN Y C , LI O . Intelligent feature extraction and real-time identification algorithm for unknown traffic data[J]. Journal of Information Engineering University, 2021,22(5): 597-605. |
[1] | Jia XU, Zhihua JIAN, Honghui JIN, Chao WU, Lin YOU, Yingxiao WU. Synthetic spoofing speech detection method based on center-symmetric local binary pattern [J]. Telecommunications Science, 2023, 39(1): 72-78. |
[2] | Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU. Spoofing speech detection algorithm based on joint feature and random forest [J]. Telecommunications Science, 2022, 38(6): 91-99. |
[3] | Liang CHEN, Yaguan QIAN, Zhiqiang HE, Xiaohui GUAN, Bin WANG, Xing WANG. A flexible pruning on deep convolutional neural networks [J]. Telecommunications Science, 2022, 38(1): 83-94. |
[4] | Zihao LIU, Xiaojun JIA, Sulan ZHANG, Zhiling XU, Jun ZHANG. Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network [J]. Telecommunications Science, 2021, 37(3): 133-145. |
[5] | Jingcheng CAO, Jidong ZHANG, Guojie SHI. A method for improving recall rate of similar image retrieval by using edge enhancement technology [J]. Telecommunications Science, 2021, 37(1): 76-84. |
[6] | Zimeng LU,Jiayi CHEN,Jing LI,Yue XIE,Xinli JIANG,Lei HAN,Qian GUO. An empty-nest power user identification method based on weighted random forest algorithm [J]. Telecommunications Science, 2020, 36(8): 112-121. |
[7] | Rongfang ZHANG,Dandan XU,Yuanguang WANG,Siyu PAN,Zhengmao LI. Application of machine learning in the fake user identification of IoT [J]. Telecommunications Science, 2019, 35(7): 136-144. |
[8] | Peng WEN,Zongju PENG,Fen CHEN,Gangyi JIANG,Mei YU. Complexity control method of random forest based HEVC [J]. Telecommunications Science, 2019, 35(2): 14-26. |
[9] | Qiang LI,Zilu KANG. Video crowd counting method based on conv-pooling deep spatial and temporal features [J]. Telecommunications Science, 2018, 34(6): 72-79. |
[10] | Chaoyi BIAN,Shaomin ZHU,Tao ZHOU. Implementation of a big data anonymization system based on Spark [J]. Telecommunications Science, 2018, 34(4): 156-161. |
[11] | Hongwei XU,Diqun YAN,Fan YANG,Rangding WANG,Chao JIN,Li XIANG. Detection algorithm of electronic disguised voice based on convolutional neural network [J]. Telecommunications Science, 2018, 34(2): 46-57. |
[12] | Yanqing WANG,Hanchen WANG. Research on a combining algorithm for harassing calls to identify [J]. Telecommunications Science, 2017, 33(7): 112-119. |
[13] | Xu DU,Jingyu FENG,Shaoqing LV,Wei SHI. PM2.5 concentration prediction model based on random forest regression analysis [J]. Telecommunications Science, 2017, 33(7): 66-75. |
[14] | Huijun XING,Shuo CHANG. Pedestrian surveillance system based on mobile vehicle [J]. Telecommunications Science, 2017, 33(2): 120-127. |
[15] | Qian LI,Hao JIANG,Jintao YANG. Individual encounter prediction based on mobile internet record data [J]. Telecommunications Science, 2017, 33(10): 115-123. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|