Skip to main content
SpringerLink
Log in

Fast Localization Model of Network Intrusion Detection System for Enterprises Using Cloud Computing Environment

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

With the advancement of network security, intrusion detection system (IDS) is increasingly used for network-connected environments. As the work of enterprises, governments, and other organizations has increasingly relied on computer network systems, protecting these systems from attacks has become a top priority. IDS has become an essential tool for safeguarding the systems with the increasing number of connected devices. To address the shortcomings of existing IDS, this research proposes an Enterprise Network for Intrusion Detection System (ENIDS) with a fast localization algorithm for cloud-based infrastructure. The proposed system detects and locates attacks by identifying abnormal domain values in the header of packets at the data link layer, network layer, and transport layer. ENIDS comprises three components: an event generator that serves as the source of event record flow, an analysis engine that checks if an attack has occurred based on the information sent by the event generator, and a reaction component that generates a response based on the results of the analysis engine. Additionally, this paper explains the fast localization model of intrusion detection for data of enterprises by explaining keyword selection methods. Experimental results show that the proposed method has a higher localization rate in comparison to direct localization, with a localization rate of 95.7% for the static targets and 92.7% for the dynamic targets. ENIDS has also been compared to existing systems using Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The proposed method has the highest accuracy (96.25%), precision (95.57%), recall (92.24%), and F1-score (93.57%). The simulation results show that the model is effective and can detect and locate the data intrusion behavior quickly.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Zhang Y, Lee W, Huang YA (2003) Intrusion detection techniques for mobile wireless networks. Wireless Netw 9:545–556

    Article  Google Scholar 

  2. Dhage SN, Meshram BB (2012) Intrusion detection system in cloud computing environment. International Journal of Cloud Computing 1(2-3):261–282

  3. Chon J, Cha H (2011) Lifemap: a smartphone-based context provider for location-based services. IEEE Pervasive Comput 10(2):58–67

    Article  Google Scholar 

  4. Hsieh CH, Chen JY, Nien BH (2019) Deep learning-based indoor localization using received signal strength and channel state information. IEEE access 7:33256–33267

    Article  Google Scholar 

  5. Ma X, Liu Y, Ouyang C (2022) Capturing semantic features to improve chinese event detection. CAAI Trans Intell Technol 7(2):219–227

    Article  Google Scholar 

  6. Lei Y (2022) Research on microvideo character perception and recognition based on target detection technology. J Comput Cogn Eng 1(2):83–87

    Google Scholar 

  7. Kong H, Lu L, Yu J, Chen Y, Tang F (2020) Continuous authentication through finger gesture interaction for smart homes using WiFi. IEEE Trans Mob Comput 20(11):3148–3162

    Article  Google Scholar 

  8. Teixeira T, Dublon G, Savvides A (2010) A survey of human-sensing: methods for detecting presence, count, location, track, and identity. ACM-CSUR 5(1):59–69

    Google Scholar 

  9. Jiang H, Wang M, Zhao P, Xiao Z, Dustdar S (2021) A utility-aware general framework with quantifiable privacy preservation for destination prediction in LBSs. IEEE/ACM Trans Networking 29(5):2228–2241

    Article  Google Scholar 

  10. Kaltiokallio O, Bocca M, Patwari N (2012) "Follow @grandma: Long-term device-free localization for residential monitoring," 37th Annual IEEE Conference on Local Computer Networks - Workshops, Clearwater, FL, USA, pp 991–998. https://doi.org/10.1109/LCNW.2012.6424092

  11. Shamshirband S, Fathi M, Chronopoulos AT, Montieri A, Palumbo F, Pescapè A (2020) Computational intelligence intrusion detection techniques in mobile cloud computing environments: review, taxonomy, and open research issues. J Inform Secur Appl 55:102582

    Google Scholar 

  12. Ribeiro J, Saghezchi FB, Mantas G, Rodriguez J, Shepherd SJ, Abd-Alhameed RA (2020) An autonomous host-based intrusion detection system for android mobile devices. Mob Networks Appl 25:164–172

    Article  Google Scholar 

  13. Li B, Zhou X, Ning Z, Guan X, Yiu KC (2022) Dynamic event-triggered security control for networked control systems with cyber-attacks: a model predictive control approach. Inf Sci 612:384–398. https://doi.org/10.1016/j.ins.2022.08.093

    Article  Google Scholar 

  14. Chen Z (2022) Research on internet security situation awareness prediction technology based on improved RBF neural network algorithm. J Comput Cogn Eng 1(3):103–108

    MathSciNet  Google Scholar 

  15. Adib F, Katabi D (2016) August. Seeing through walls with wireless signals. SIGCOMM '13: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM, pp 75–86. https://doi.org/10.1117/2.1201601.006311

  16. Lv J, Man D, Yang W, Du X, Yu M (2017) Robust WLAN-based indoor intrusion detection using PHY layer information. IEEE Access 6:30117–30127

    Article  Google Scholar 

  17. Want R, Hopper A, Falcao V, Gibbons J (1992) The active badge location system. ACM Trans Inform Syst (TOIS) 10(1):91–102

    Article  Google Scholar 

  18. Liu G (2021) Data collection in mi-assisted wireless powered underground sensor networks: directions, recent advances, and challenges. IEEE Commun Mag 59(4):132–138

    Article  Google Scholar 

  19. Xiao Z, Shu J, Jiang H, Lui JCS, Min G, Liu J,... Dustdar S (2022) Multi-objective parallel task offloading and content caching in D2D-aided MEC Networks. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2022.3199876

  20. Lu S, Ban Y, Zhang X, Yang B, Liu S, Yin L, Zheng W (2022) Adaptive control of time delay teleoperation system with uncertain dynamics. Front Neurorobot 16:928863. https://doi.org/10.3389/fnbot.2022.928863

    Article  Google Scholar 

  21. Sun Y, Ma P, Dai J, Li D (2022) A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed-wing patrol aircraft. Ecological Modelling, p 418

  22. Ni LM, Liu Y, Lau YC, Patil AP (2003) Landmarc: Indoor location sensing using active RFID. Pervasive Computing and Communications, 2003. (PerCom 2003). In: Proceedings of the First IEEE International Conference on. IEEE

  23. Chan YT, Hang HYC, Ching PC (2006) Exact and approximate maximum likelihood localization algorithms. IEEE Trans Veh Technol 55(1):10–16

    Article  Google Scholar 

  24. Zhang D, Ma J, Chen Q, Ni LM (2007) March. "An RF-Based System for Tracking Transceiver-Free Objects". In: Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07), White Plains, NY, USA, 2007, pp 135–144. https://doi.org/10.1109/PERCOM.2007.8

  25. Fernandes R, Matos JN, Varum T, Pinho P (2014) "Wi-Fi intruder detection," 2014 IEEE Conference on Wireless Sensors (ICWiSE), Subang, Malaysia, pp 96–99. https://doi.org/10.1109/ICWISE.2014.7042668

  26. Wallbaum M, Diepolder S (2006) October. A motion detection scheme for wireless LAN stations. The 3rd international conference on mobile computing and ubiquitous networking, pp 2–9

  27. Sun Z, Xu Y, Liang G, Zhou Z (2017) An intrusion detection model for wireless sensor networks with an improved V-detector algorithm. IEEE Sens J 18(5):1971–1984

    Article  Google Scholar 

  28. Li B, Tan Y, Wu A, Duan G (2021) A distributionally robust optimization based method for stochastic model predictive control. IEEE Trans Autom Control 67(11):5762–5776. https://doi.org/10.1109/TAC.2021.3124750

    Article  MathSciNet  Google Scholar 

  29. Sudqi Khater B, Abdul Wahab AWB, Idris MYIB, Abdulla Hussain M, Ahmed Ibrahim A (2019) A lightweight perceptron-based intrusion detection system for fog computing. Appl Sci 9(1):178

    Article  Google Scholar 

  30. Haseeb K, Islam N, Almogren A, Din IU (2019) Intrusion prevention framework for secure routing in WSN-based mobile internet of things. Ieee Access 7:185496–185505

    Article  Google Scholar 

  31. Usman M, Jan MA, He X, Chen J (2019) A survey on representation learning efforts in cybersecurity domain. ACM Comput Surv (CSUR) 52(6):1–28

    Article  Google Scholar 

  32. Khraisat A, Gondal I, Vamplew P, Kamruzzaman J (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1):1–22

    Article  Google Scholar 

  33. Caminero G, Lopez-Martin M, Carro B (2019) Adversarial environment reinforcement learning algorithm for intrusion detection. Comput Netw 159:96–109

    Article  Google Scholar 

  34. Li M, Tian Z, Du X, Yuan X, Shan C.,..., Guizani M (2023). Power normalized cepstral robust features of deep neural networks in a cloud computing data privacy protection scheme. Neurocomputing 518:165–173. https://doi.org/10.1016/j.neucom.2022.11.001

  35. Liang J, Jing T, Niu H, Wang J (2020) Two-terminal fault location method of distribution network based on adaptive convolution neural network. IEEE Access 8:54035–54043

    Article  Google Scholar 

  36. Yu J, Lu L, Chen Y, Zhu Y, Kong L (2021) An indirect eavesdropping attack of Keystrokes on Touch screen through Acoustic Sensing. IEEE Trans Mob Comput 20(2):337–351. https://doi.org/10.1109/TMC.2019.2947468

    Article  Google Scholar 

  37. Dai X, Xiao Z, Jiang H, Alazab M, Lui JCS, Min G,..., Liu J (2023) Task offloading for cloud-assisted fog computing with dynamic service caching in enterprise management systems. IEEE Trans Ind Inform 19(1), 662–672. https://doi.org/10.1109/TII.2022.3186641

Download references

Acknowledgements

Natural Science Foundation of Hunan Province, China.

Research on Key Technologies of enterprise network security and protection system in cloud computing environment Grant NO. 2020JJ6062.

Author information

Authors and Affiliations

  1. School of Computer and Electrical Engineering, Hunan University of Arts and Science, Hunan, 415000, China

    Xingzhu Wang

Authors
  1. Xingzhu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingzhu Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X. Fast Localization Model of Network Intrusion Detection System for Enterprises Using Cloud Computing Environment. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02176-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-023-02176-w

Keywords

Search

Navigation