Unknown Protocol Data Frame Classification Algorithm based on Improved K-Means

doi:10.23940/ijpe.20.11.p4.17211731

Abstract

Abstract: Aiming at the problem of low efficiency and low accuracy in the classification of multiple unknown protocol data frames in an unknown network environment, this paper proposes a k-means clustering algorithm based on information entropy and density. First, according to the characteristics of the protocol data frame in the bitstream form, the Euclidean distance between the data frames is weighted by using information entropy. Then, the high-density data frame set is determined by the statistics of the density of each data frame, and the cluster center point is determined in this set by the maximum and minimum distance criterion. Finally, the ratio of the distance in the protocol cluster to the distance between the clusters is used to determine the number of unknown protocols. Simulation results show that this method can cluster the data frames of unknown bitstream protocol quickly and accurately.

Key words: data frame, bit stream, information entropy, K-means

Zhiguo Liu and Changqing Ren. Unknown Protocol Data Frame Classification Algorithm based on Improved K-Means [J]. Int J Performability Eng, 2020, 16(11): 1721-1731.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. X. Hei, B. Bai, Y. Wang, L. Zhang, L. Zhu,W. Ji, “Feature Extraction Optimization for Bitstream Communication Protocol Format Reverse Analysis,” inProceedings of the 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 662-669, Rotorua, New Zealand, 2019
2. W. Wang, B. Bai, Y. Wang, X. Hei,L. Zhang, “Bitstream Protocol Classification Mechanism based on Feature Extraction,” in Proceedings of the2019 International Conference on Networking and Network Applications (NaNA), pp. 241-246, Daegu, Korea(South), 2019
3. F. Meng, C. Zhang,G. Wu, “Protocol Reverse based on Hierarchical Clustering and Probability Alignment from Network Traces,” inProceedings of the 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), pp. 443-447, Shanghai, 2018
4. M. G. Kibria, K. Nguyen, G. P. Villardi, O. Zhao, K. Ishizu,F. Kojima, “Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks,”IEEE Access, Vol. 6, pp. 32328-32338, 2018
5. L. Cai, R. Shi,D. Xu, “Communication Protocol Identification based on Data Mining and Automatic Reasoning,” inProceedings of the 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 211-216, Beijing, 2017
6. F. Li, T. Li, C. R. Zhang, Y. Wang,J. Song, “Length Identification of Unknown Data Frame,” inProceedings of the 2012 Eighth International Conference on Computational Intelligence and Security, pp. 674-677, Guangzhou, 2012
7. H. Yang, P. Li, Q. Zhu,L. Xu, “The Application Layer Protocol Identification Method based on Semisupervised Learning,” inProceedings of the 2011 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 115-120, Sydney, NSW, 2011
8. S. Tao, H. Yu,Q. Li, “Bit-Oriented Format Extraction Approach for Automatic Binary Protocol Reverse Engineering,” IET Communications, Vol. 10, No. 6, pp. 709-716, 2016
9. F. Z. Zhang, H. C. Zhou, J. J. Zhang, Y. Liu,C. R Zhang, “A Protocol Classification Algorithm based on Improved AGNES,” Computer Engineering and Science, Vol. 39, No. 4, pp. 796-803, 2017
10. Z. F.Wang and Y. Wu, “Unknown Protocol Bitstream Clustering based on Improved K-Means Algorithm,” Journal of Computer Applications, Vol. 36, No. S1, pp. 5-8, 2016
11. X. Yan, Q. Li,S. Tao, “A Clustering Algorithm for Binary Protocol Data Frames based on Principal Component Analysis and Density Peaks Clustering,” inProceedings of the 2017 IEEE 17th International Conference on Communication Technology (ICCT), pp. 1260-1266, Chengdu, 2017
12. F. Z. Zhang, H. C. Zhou, J. J. Zhang, Y. Liu,C. R Zhang, “Unknown Bit-Stream Protocol Classification Model with Zero-knowledge,” Computer Science, Vol. 43, No. 8, pp. 39-44, 2016
13. Z. F.Wang and G. L. Shan, “Survey on Bit Stream Oriented Unknown Protocol Identification and Analysis Techniques,” Application Research of Computers, Vol. 53, No. 3, pp. 80-86, 2017
14. D. Lei, T. Wang, J. P. Zhao,Y. F. Ma, “Survey on Bit Stream Oriented Unknown Protocol Identification and Analysis Techniques,” Application Research of Computers, Vol. 33, No. 11, pp.3206-3210+3250, 2016
15. Z. Z. Shao. Y. Yan,Y. L. Min, “The Research of Protocol Identification based on Traffic Analysis,” inProceedings of the 2017 10th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 172-175, Changsha, 2017
16. C. H.Cao and Y. K. Lei, “Journal of Chinese Computer Systems,” Journal of Chinese Computer Systems, Vol. 39, No. 2, pp. 297-303, 2018
17. J. G. Sun, J. Liu,L. Y. Zhao, “Clustering Algorithms Research,” Journal of Software, Vol.2008, No. 1, pp. 48-61, 2008
18. X. Y.Yan and Q. Li, “Clustering Algorithm for Binary Protocol Data Frames Combining Feature Dimensionality Reduction and Density Peans Clustering,” Journal of Chinese Computer Systems, Vol. 39, No. 12, pp. 2662-2668, 2018
19. J. Zheng and J. P. Li, “Feature Identification of Unknown Protocol,” inProceedings of the 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 147-149, Chengdu, 2016
20. S. L. Yang, Y. S. Li, X. X. Hu,R. Y. Pan, “Optimization Study on k Value of K-Means Algorithm,” Systems Engineering-Theory and Practice, Vol.2006, No. 2, pp. 97-101, 2006

[1]	Deepak Kumar, Chaman Verma, Purushottam Sharma, Deeksha Kumari, and Zoltán Illés. Demographic and Clinical Factors Role Identification in Stroke Risk and Subtype Prediction [J]. Int J Performability Eng, 2023, 19(6): 368-378.
[2]	Ashima Arya and Sanjay Kumar Malik. Software Fault Prediction using K-Mean-Based Machine Learning Approach [J]. Int J Performability Eng, 2023, 19(2): 133-143.
[3]	Arvind Kumar Mishra, Renuka Nagpal, Kirti Seth, and Rajni Sehgal. Maintainability of Service-Oriented Architecture using Hybrid K-means Clustering Approach [J]. Int J Performability Eng, 2023, 19(1): 33-42.
[4]	Kai-Wen Chen and Chin-Yu Huang. Automatic Categorization of Software with Document Clustering Methods and Voting Mechanism [J]. Int J Performability Eng, 2022, 18(4): 251-262.
[5]	Kajal Dwivedi, Ramanathan Lakshmanan, and Rajeshkannan Regunathan. K-means Under-Sampling for Hypertension Prediction using NHANES Dataset [J]. Int J Performability Eng, 2021, 17(8): 733-740.
[6]	M. Muzammil Parvez, J. Shanmugam, and V.S. Ghali. Fuzzy C-based Automatic Defect Detection using Barker Coded Thermal Wave Imaging [J]. Int J Performability Eng, 2021, 17(5): 484-490.
[7]	Huaiguang Wu, Pengjie Xie, Ming Cheng, and Hongwei Tao. A Hybrid Model of Predicting Breast Cancer Survivability based on Specific Stages [J]. Int J Performability Eng, 2020, 16(8): 1183-1192.
[8]	Chulei Zhang, Honghua Cui, Yizhang Wang, Tiantian Zhao, and You Zhou. LDKM: An Improved K-Means Algorithm with Linear Fitting Density Peak [J]. Int J Performability Eng, 2020, 16(3): 454-461.
[9]	Lian Yu, Lei Zhang, Cong Tan, Bei Zhao, Chen Zhang, and Lijun Liu. Repeatedly Coding Inter-Packet Delay for Tracking Down Network Attacks [J]. Int J Performability Eng, 2020, 16(2): 265-283.
[10]	Qinggang Wu and Xueming Zhai. Remote Sensing Object Detection via an Improved YOLO Network [J]. Int J Performability Eng, 2020, 16(11): 1803-1813.
[11]	Mingzhu Li and Yufeng Deng. A Machine Learning-based Building Operational Pattern Identification [J]. Int J Performability Eng, 2020, 16(11): 1835-1844.
[12]	Wenjie Li. Imbalanced Data Optimization Combining K-Means and SMOTE [J]. Int J Performability Eng, 2019, 15(8): 2173-2181.
[13]	Hongchan Li and Ni Yao. Four-Layer Feature Selection Method for Scientific Literature based on Optimized K-Medoids and Apriori Algorithms [J]. Int J Performability Eng, 2019, 15(4): 1141-1150.
[14]	Ming Lei, Bin Wen, Jianhou Gan, and Jun Wang. Clustering Algorithm of Ethnic Cultural Resources based on Spark [J]. Int J Performability Eng, 2019, 15(3): 756-762.
[15]	Yan Zhang, Li Qiao, Xingya Wang, Jingying Cai, and Xuefei Liu. Automatic Software Testing Target Path Selection using K-Means Clustering Algorithm [J]. Int J Performability Eng, 2019, 15(10): 2667-2674.