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Complex application identification and private network mining algorithm based on traffic-aware model in large-scale networks

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Abstract

Due to the lack of definition and analysis of complex applications in the field of protocol identification research, complex application identification is still attributed to traditional protocol identification, resulting in poor recognition performance. This paper has conducted in-depth research on complex application identification problems. Complex applications are first defined and distinguished from traditional protocols. At the same time, the communication process of complex applications is deeply analyzed, and the communication characteristics of complex applications are summarized. Then, based on the communication characteristics, a traffic-aware model describing the communication process of complex applications is proposed. The communication modes of complex applications are modeled from spatial dimension, time dimension and traffic dimension. Based on the traffic-aware model, spatial dimension awareness is used to filter network traffic. Finally, the traffic dimension is used to cluster the filtered multiple network flows into multiple network flow clusters and extract statistical features. The time dimension is used to construct the behavior state sequence of the complex application based on the statistical characteristics of the network flow cluster, and finally the behavior state is used. Sequences are used as identification features to effectively and accurately identify complex applications. The experimental results show that the accuracy of Skype recognition is improved from 25% to 80% after the original method is improved.

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References

  1. Darwish TSJ, Bakar KA, Haseeb K (2018) Reliable intersection-based traffic aware routing protocol for urban areas vehicular ad hoc networks[J]. IEEE Intell Transp Syst Mag 10(1):60–73

    Article  Google Scholar 

  2. Goudarzi F, Asgari H, Al-Raweshidy HS (2018) Traffic-aware VANET routing for City environments—a protocol based on ant Colony optimization[J]. IEEE Syst J, PP(99):1–11

  3. Li P, Chen Z, Yang LT et al (2018) An improved stacked auto-encoder for network traffic flow classification[J]. IEEE Netw 32(6):22–27

    Article  Google Scholar 

  4. Bhuiyan MZA, Wang G, Jie W et al (2017) Dependable structural health monitoring using wireless sensor networks[J]. IEEE Trans Depend Sec Comput 14(4):363–376

    Article  Google Scholar 

  5. Karimi-Majd AM, Fathian M, Amiri B (2015) A hybrid artificial immune network for detecting communities in complex networks[J]. Computing 97(5):483–507

    Article  MathSciNet  Google Scholar 

  6. Jeong S, Lee D, Hyun J et al. (2017) Application-aware traffic engineering in software-defined network[C]. Network Operations & Management Symposium. IEEE 2017:1–6

  7. Li G, Dong M, Ota K, et al. (2017) Deep packet inspection based application-aware traffic control for software defined networks[C]. Global communications conference. IEEE

  8. Liu L, Wang S, Su G, Huang ZG, Liu M (2017) Towards complex activity recognition using a Bayesian network-based probabilistic generative framework[J]. Pattern Recogn 68(C):295–309

    Article  Google Scholar 

  9. Rewadkar D, Doye D (2018) Multi-objective auto-regressive whale optimisation for traffic-aware routing in urban VANET[J]. IET Inf Secur 12(4):293–304

    Article  Google Scholar 

  10. Chang TC, Lin CH, Lin CJ et al (2019) Traffic-aware sensor grouping for IEEE 802.11ah networks: regression based analysis and design[J]. IEEE Trans Mob Comput 18(3):674–687

    Article  Google Scholar 

  11. Baffier J F, Suppakitpaisarn V (2015) Algorithms for finding robust and sustainable network flows against multilink-attack[C]. Int Workshop Reliab Netw Des Model2015:251–258

  12. Tian X, Zhao R (2015) Energy network flow model and optimization based on energy hub for big harbor Industrial Park[J]. J Coast Res 73:298–303

    Article  Google Scholar 

  13. Chen T, Sun J, Wu W et al (2015) Optimal bandwidth allocation for hybrid video-on-demand streaming with a distributed max flow algorithm[J]. Comput Netw Int J Comput Telecom Netw 91(C):483–494

    Google Scholar 

  14. Nguyen QN, Arifuzzaman M, Yu K, Sato T (2018) A context-aware green information-centric networking model for future wireless communications[J]. IEEE Access 6(99):22804–22816

    Article  Google Scholar 

  15. Chen Q, Xuan S, Fan Z, et al. (2018) A context-aware nonnegative matrix factorization framework for traffic accident risk estimation via heterogeneous data[C]. 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

  16. Jakalan A, Gong J, Su Q, Hu X, Abdelgder AMS (2016) Social relationship discovery of IP addresses in the managed IP networks by observing traffic at network boundary.[J]. Comput Netw 100:12–27

    Article  Google Scholar 

  17. Jouzdani J, Fathian M, Jouzdani J et al (2016) Hybrid electromagnetism-like algorithm for dynamic supply chain network design under traffic congestion and uncertainty[J]. Math Probl Eng 2016:1):1–1)18

    Article  Google Scholar 

  18. Si L, Wang Z, Liu X, Tan C, Xu J, Zheng K (2015) Multi-sensor data fusion identification for shearer cutting conditions based on parallel quasi-Newton neural networks and the Dempster-Shafer theory[J]. Sensors 15(11):28772–28795

    Article  Google Scholar 

  19. Sheng C, Xia H, Khalaf E, et al. (2016) Complex-valued B-spline neural network and its application to iterative frequency-domain decision feedback equalization for Hammerstein communication systems[C]. Int Joint Conf Neural Netw 2016:4097–4104

  20. Teng Y, Liang W, Zhang Y, Yang R (2018) Traffic-aware resource allocation scheme for mMTC in dynamic TDD systems[J]. IET Commun 12(15):1910–1918

    Article  Google Scholar 

  21. Schaerer J, Zhao Z, Braun T (2018). DTARP: A dynamic traffic aware routing protocol for wireless sensor networks[C]. Proceedings of the 7th International Workshop on Real-World Embedded Wireless Systems and Networks

  22. Dabaghi-Zarandi F, Movahedi Z (2018) A dynamic traffic-aware energy-efficient algorithm based on sleep-scheduling for autonomous systems[J]. Computing 100:10):1–10)21

    Article  Google Scholar 

  23. Shi W, Gao D, Zhou H, et al. (2018) Traffic aware inter-layer contact selection for multi-layer satellite terrestrial network[C]. Globecom IEEE Glob Commun Conf 2018:1–7

  24. Zhu LH, Zhang QY, Shen M et al (2017) Efficient traffic-aware routing scheme for software defined networks[J]. J Northeastern Univ 38(3):335–340

    MathSciNet  Google Scholar 

  25. Knobloch F, Braunschweig N (2017) A traffic-aware moving light system featuring optimal energy efficiency[J]. IEEE Sensors J 17(23):7731–7740

    Article  Google Scholar 

  26. Akila T H, Siriweera S, Paik I, et al. (2017) QoS-aware traffic-efficient web service selection over BigData space[C]. IEEE Int Conf Comput Inf Technol 2017:197–203

Download references

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Authors and Affiliations

  1. Continuing Education College, Shandong University, Weihai, Weihai, 264209, Shandong, China

    Rongyu Tian

  2. Academic Affairs Office, Shandong University, Weihai, Weihai, 264209, Shandong, China

    Hui Zhu

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  1. Rongyu Tian
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  2. Hui Zhu
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Correspondence to Rongyu Tian.

Additional information

This article is part of the Topical Collection: Special Issue on Fog/Edge Networking for Multimedia Applications

Guest Editors: Yong Jin, Hang Shen, Daniele D'Agostino, Nadjib Achir, and James Nightingale

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Tian, R., Zhu, H. Complex application identification and private network mining algorithm based on traffic-aware model in large-scale networks. Peer-to-Peer Netw. Appl. 12, 1594–1605 (2019). https://doi.org/10.1007/s12083-019-00803-6

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  • DOI: https://doi.org/10.1007/s12083-019-00803-6

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