Skip to main content
Log in

Design of Multimedia Education Network Security and Intrusion Detection System

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The existing multimedia wireless network security detection technology cannot meet the requirements of wireless network security detection. Therefore, based on the special security requirements of multimedia wireless networks, this paper constructed a multimedia wireless network security detection system that meets the actual situation of the organization. The system was designed using the B/S architecture and used Django as the framework for system development. The system presentation layer mainly presents the system page to the user and passes the user request. Simultaneously, in this paper, the application of PrefixSpan algorithm in anomaly detection was studied and experimental analysis was carried out. The experimental results are in line with expectations. This verifies the effectiveness of the proposed system and provides a theoretical reference for subsequent related research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Aburomman AA, Reaz MBI (2017) A survey of intrusion detection systems based on ensemble and hybrid classifiers[J]. Computers Security 65:135–152

    Article  Google Scholar 

  2. Adhi TB, Kyung-Hyune R (2017) HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System[J]. IEICE Transactions on Information and Systems E100.D(8):1729–1737

    Article  Google Scholar 

  3. Agrawal N, Tapaswi S (2017) The performance analysis of honeypot based intrusion detection system for wireless network[J]. Int J Wireless Inf Networks 24(1):14–26

    Article  Google Scholar 

  4. Al-Yaseen WL, Othman ZA, Nazri MZA (2017) Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system[J]. Expert Syst Appl 67:296–303

    Article  Google Scholar 

  5. Deng L, Li D, Yao X et al (2018) Mobile network intrusion detection for IoT system based on transfer learning algorithm[J]. Clust Comput 2:1–16

    Google Scholar 

  6. Ganesh SS, Ramar K (2017) A cluster based intrusion detection system for homogeneous and heterogeneous Mobile ad hoc network[J]. J Comput Theor Nanosci 14(9):4249–4254

    Article  Google Scholar 

  7. Haider W, Hu J, Slay J et al (2017) Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling[J]. J Netw Comput Appl 87:185–192

    Article  Google Scholar 

  8. Hajisalem V, Babaie S (2018) A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection[J]. Comput Netw 136:37–50

    Article  Google Scholar 

  9. Harang R, Kott A (2017) Burstiness of intrusion detection process: Empirical evidence and a modeling approach. IEEE Transactions on Information Forensics and Security 12(10):2348–2359

    Article  Google Scholar 

  10. Idhammad M, Karim A, Belouch M (2018) Distributed intrusion detection system for cloud environments based on data mining techniques[J]. Procedia Comput Sci 127:35–41

    Article  Google Scholar 

  11. Zhou C, Huang S, Xiong N, et al (2015) Design and analysis of multimodel-based anomaly intrusion detection systems in industrial process automation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 45(10):1345–1360

    Article  Google Scholar 

  12. Li P, Wang Z, Xu H et al (2017) Intrusion detection methods based on incomplete RFID traces[J]. Chin J Electron 26(4):675–680

    Article  Google Scholar 

  13. Lin CH, Song KT (2014) Probability-based location aware design and on-demand robotic intrusion detection system[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(6):705–715

    Article  Google Scholar 

  14. Massato Kakihata E, Molina Sapia H, Toshiaki Oiakawa R et al (2017) Intrusion detection system based on flows using machine learning algorithms[J]. IEEE Lat Am Trans 15(10):1988–1993

    Article  Google Scholar 

  15. Mehmood A, Khanan A, Umar MM et al (2017) Secure Knowledge and Cluster-Based Intrusion Detection Mechanism for Smart Wireless Sensor Networks[J]. IEEE Access PP(99):1–1

    Google Scholar 

  16. Muhammet B, Resul D (2018) A novel honeypot based security approach for real-time intrusion detection and prevention systems[J]. Journal of Information Security and Applications 41:103–116

    Article  Google Scholar 

  17. Marteau PF (2018) Sequence covering for efficient host-based intrusion detection. IEEE Transactions on Information Forensics and Security 14(4):994–1006

    Article  Google Scholar 

  18. Sultana N, Chilamkurti N, Peng W et al (2019) Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications 12(2):493–501

    Article  Google Scholar 

  19. Vespa L, Weng N, Ramaswamy R (2011) MS-DFA: multiple-stride pattern matching for scalable deep packet inspection[J]. Comput J 54(2):285–303

    Article  Google Scholar 

  20. Xingshui Z, Feng G, Jingchang H et al (2017) Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array[J]. Sensors 17(3):514

    Article  Google Scholar 

Download references

Acknowledgements

The research presented in this paper is supported in part by the National Natural Science Foundation (No. 61602370).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li Zhu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Zhu, L. & Liu, F. Design of Multimedia Education Network Security and Intrusion Detection System. Multimed Tools Appl 79, 18801–18814 (2020). https://doi.org/10.1007/s11042-020-08724-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08724-w

Keywords

Navigation