nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

Instant Messaging Application Traffic Recognition

verfasst von : Pu Wang, Xinrun Lyu, Xiangzhan Yu, Chong Zhang

Erschienen in: Advances in Artificial Intelligence and Security

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

As a basic work of network security, network traffic recognition plays an important role in network resource management and abnormal network traffic monitoring. At present, network traffic identification has become one of the hottest issues in academic research. In the past research, network traffic analysis was mainly done by Port Matching, Deep Packet Inspection. However, these methods are not perfect, and they are not suitable for today. This paper implements a traffic recognition method based on deep learning and machine learning. Besides, this paper implements unsupervised clustering of traffic. On the UNB ISCX data set, the experimental results are quite good.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Research on the Picture Database of Minority Emotion

Touch, J., Mankin, A., Kohlere, et al.: Service name and transport protocol port number registry [EB/OL] [2019-04-14]. https://www.iana.org/assignments/service-names-port-numbers/service-names-port-numbers.xhtml

Moore, A.W., Zuev, D.: Internet traffic classification using Bayesian analysis techniques. ACM SIGMETRICS Perform. Eval. Rev. 33(1), 50–60 (2005)CrossRef

Tran, T.N., Drab, K., Daszykowski, M.: Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemom. Intell. Lab. Syst. 120, 92–96 (2013) CrossRef

Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRef

Dong, J., Wang, F., Yuan, B.: Accelerating BIRCH for clustering large scale streaming data using CUDA dynamic parallelism. In: Yin, H., Tang, Ke., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 409–416. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41278-3_50CrossRef

Aranganayagi, S., Thangavel, K.: Clustering categorical data using silhouette coefficient as a relocating measure, pp. 13–17 (2007)

Vita, G., Kamboj, P.: Content based image processing approach on colored images using KNN classifier. Int. J. Emerg. Trends Technol. Comput. Sci. 36(4), 183–191 (2016)CrossRef

Chang, B.M., Tsai, H.H., Yen, C.Y.: SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains. Eng. Appl. Artif. Intell. 52, 96–107 (2016)CrossRef

Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39964-3_62CrossRef

10.

Hao Zhang, J.M., Berg, A.C., Maire, M.: SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2126–2136 (2006)

11.

Hinton, G.E., Srivastava, N., Krizhevsky, A., et al.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)

12.

Madhukar, A., Williamson, C.: A longitudinal study of P2P traffic classification. In: Proceedings of the 14th International Symposium on Modeling, Analysis, and Simulation, pp. 179–188. IEEE, Piscataway (2006)

13.

Chen, H., Hu, Z., Ye, Z., et al.: A new model for P2P traffic identification based on DPI and DFI. In: Proceedings of the 2009 International Conference on Information Engineering and Computer Science, pp. 1–3. IEEE, Piscataway (2009)

14.

Wang, C., Zhou, X., You, F., et al.: Design of P2P traffic identification based on DPI and DFI. In: Proceedings of the 2009 International Symposium on Computer Network and Multimedia Technology, pp. 1–4. `IEEE, Piscataway (2009)

15.

Li, Y., Qi, H., et al.: Joint embeddings of shapes and images via CNN image purification. ACM Trans. Graph. 34(6), 1–12 (2015)

16.

Yu, K., Guo, G.-D., Li, J., Lin, S.: Quantum algorithms for similarity measurement based on Euclidean distance. Int. J. Theor. Phys. 59(10), 3134–3144 (2020). https://doi.org/10.1007/s10773-020-04567-1MathSciNetCrossRefMATH

17.

Suwanda, R., Syahputra, Z., Zamzami, E.M.: Analysis of Euclidean distance and Manhattan distance in the K-means algorithm for variations number of centroid K. J. Phys. Conf. Ser. 1566(1), 012058 (6pp) (2020)

18.

Mahsereci, M., Balles, L., Lassner, C., et al.: Early stopping without a validation set (2017)

19.

Sumkin, J.H., Ganott, M.A., Chough, D.M., et al.: Recall rate reduction with tomosynthesis during baseline screening examinations: an assessment from a prospective trial. Acad. Radiol. 22, 1477–1482 (2015)CrossRef

20.

Choi, M.: An empirical study on the optimal batch size for the deep Q-network. In: Kim, J.-H., Myung, H., Kim, J., Xu, W., Matson, E.T., Jung, J.-W., Choi, H.-L. (eds.) RiTA 2017. AISC, vol. 751, pp. 73–81. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-78452-6_8CrossRef

21.

Freier, A., Karlton, P., Kocher, P.: The Secure Sockets Layer (SSL) protocol version 3.0. The syncretic religion of Lin Chao-en, Columbia University Press (2011)

22.

Mclane, T.L., Mullen, S.P., Tenginakai, J.B.: Secure shell used to open a user’s encrypted file system keystore: US (2013)

23.

Ando, R.: Asura: a huge PCAP file analyzer for anomaly packets detection using massive multithreading. In: DEFCON26 (2018)

24.

Chen, H.N., Chung, K.L., Hung, J.E.: Novel fractal image encoding algorithm using normalized one-norm and kick-out condition. Image Vis. Comput. 28(3), 518–525 (2010)CrossRef

Titel: Instant Messaging Application Traffic Recognition
verfasst von: Pu Wang
Xinrun Lyu
Xiangzhan Yu
Chong Zhang
Verlag: Springer International Publishing
Buch: Advances in Artificial Intelligence and Security
Print ISBN: 978-3-030-78617-5

Electronic ISBN: 978-3-030-78618-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-78618-2_60

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner