Skip to main content
Log in

RETRACTED ARTICLE: A novel distributed training on fog node in IoT backbone networks for security

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

This article was retracted on 27 December 2022

This article has been updated

Abstract

Security is a significant issue with ubiquitous connectivity, more so with the widespread adoption of Internet of Things (IoT). The novelty of attacks with each passing day poses a conundrum to the organizations and sectors deploying the IoT. The fact remains that conventional cybersecurity frameworks face the trouble of distinguishing unknown attacks in most scenarios. Recent studies explore the endless capabilities of machine learning (ML) in reinstating the security of the IoT infrastructure. ML includes the capability of self-study and training for discovering the path for security breach and attack detection. The training methodology and progression in ML is superior to centralized detection systems. A novel methodology of distributed training on fog node in the IoT architecture with exchange of parameters (DT-FN) enhancement of intelligence through machine learning is proposed in this paper. The analyses have demonstrated that appropriated assault recognition framework has outsmarted the incorporated discovery frameworks utilizing ML model. Intrusion detection system of IoT has been incorporated on DL for more effectiveness and identification of higher level security threats. Target prejudgement-based interruption identification framework for IoT has also been discussed. The key metrics that are considered are detection rate, detection accuracy, false alarm rate, F1 measurement, precision and recall.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Change history

References

  • Alrawais A, Alhothaily A, Hu C, Cheng X (2017) Fog computing for the internet of things: security and privacy issues. IEEE Internet Comput 21(2):34–42

    Article  Google Scholar 

  • Chen AG, Wang ST (2016) Fuzzy clustering algorithm based on multiple medoids for large-scale data. Control Decis 31(12):2122–2130

    MATH  Google Scholar 

  • Costa Gondim JJ, de Oliveira Albuquerque R, Clayton Alves Nascimento A, García Villalba LJ, Kim TH (2016) A methodological approach for assessing amplified reflection distributed denial of service on the internet of things. Sensors 16(11):1855

    Article  Google Scholar 

  • Duraipandian M, Mr AND, Vinothkanna R (2019) Cloud based internet of things for smart connected objects. J Ismac 1(02):111–119

    Google Scholar 

  • Elrawy MF, Awad AI, Hamed HFA (2018) Intrusion detection systems for IoT-based smart environments: a survey. J Cloud Comput 7(1):21

    Article  Google Scholar 

  • Gao N, Gao L, He YY (2016) Deep belief nets model oriented to intrusion detection system. Syst Eng Electron 38(9):2201–2207

    Google Scholar 

  • Haoxiang W (2019) Trust management of communication architectures of internet of things. J Trends Comput Sci Smart Technol 1(02):121–130

    Article  Google Scholar 

  • Ibrahim MH (2016) Octopus: an edge-fog mutual authentication scheme. IJ Netw Secur 18(6):1089–1101

    Google Scholar 

  • Jiang J, Wang ZF, Chen TM, Zhu CC, Chen B (2015) Adaptive AP clustering algorithm and its application on intrusion detection. J Commun 36(11):118–126

    Google Scholar 

  • Kolias C, Kambourakis G, Stavrou A, Gritzalis S (2015) Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Commun Surv Tutor 18(1):184–208

    Article  Google Scholar 

  • La QD et al (2019) Enabling intelligence in fog computing to achieve energy and latency reduction. Dig Commun Netw 5(1):3–9

    Article  Google Scholar 

  • Li GD, Hu JP, Xia KW (2015) Intrusion detection using relevance vector machine based on cloud particle swarm optimization. Control Decis 30:698–702

    Google Scholar 

  • Liu B, Xia SX, Zhou Y, Han XD (2012) A sample-weighted possibilistic fuzzy clustering algorithm. Acta Electron Sin 40(2):371–375

    Google Scholar 

  • Liu L, Xu B, Zhang X, Wu X (2018) An intrusion detection method for internet of things based on suppressed fuzzy clustering. EURASIP J Wirel Commun Netw 2018(1):113

    Article  Google Scholar 

  • Midi D et al (2017) Kalis—a system for knowledge-driven adaptable intrusion detection for the internet of things. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS). http://dx.doi.org/10.1109/icdcs.2017.104

  • Mugunthan SR (2019) Security and privacy preserving of sensor data localization based on internet of things. J Ismac 1(02):81–91

    Article  Google Scholar 

  • Okay FY, Ozdemir S (2016) A fog computing based smart grid model. In: 2016 International symposium on networks, computers and communications (ISNCC). http://dx.doi.org/10.1109/isncc.2016.7746062

  • Pandian AP (2019) Enhanced edge model for big data in the internet of things based applications. J Trends Comput Sci Smart Technol 1(01):63–73

    Article  Google Scholar 

  • Qi’an W, Bing C (2012) Intrusion detection system using CVM algorithm with extensive kernel methods. J Comput Res Dev 5:1–12

    Google Scholar 

  • Sivaganesan D (2019) Block chain enabled internet of things. J Inf Technol 1(01):1–8

    Google Scholar 

  • Smys S, Raj JS (2019) Internet of things and big data analytics for health care with cloud computing. J Inf Technol 1(01):9–18

    Google Scholar 

  • Stojmenovic I, Wen S (2014) The fog computing paradigm: scenarios and security issues. In: 2014 federated conference on computer science and information systems. IEEE, pp 1–8

  • Tang C, Liu P, Tang S, Xie Y (2015) Anomaly intrusion behavior detection based on fuzzy clustering and features selection. J Comput Res Dev 52(3):718–728

    Google Scholar 

  • Thing VL (2017) IEEE 802.11 network anomaly detection and attack classification: a deep learning approach. In: 2017 IEEE wireless communications and networking conference (WCNC). IEEE, pp 1–6

  • Tong WM, Liang JQ, Lu L, Jin XJ (2015) Intrusion detection scheme based node trust value in WSNs. Syst Eng Electron 37(7):1644–1649

    Google Scholar 

  • Wu XN, Peng XJ, Yang YY, Fang K (2015) Two-level feature selection method based on SVM for intrusion detection. J Commun 36(4):2015127-1

    Google Scholar 

  • Yang YH, Huang HZ, Shen QN, Wu ZH, Zhang Y (2014) Research on intrusion detection based on incremental GHSOM. Chin J Comput 37(5):1216–1224

    Google Scholar 

  • Yi S, Qin Z, Li Q (2015) Security and privacy issues of fog computing: a survey. In: International conference on wireless algorithms, systems, and applications. Springer, Cham, pp 685–695

  • Yu Y, Huang H (2007) Ensemble approach to intrusion detection based on improved multi-objective genetic algorithm. J Softw 18(6):1369–1378

    Article  Google Scholar 

  • Zhang L, Bai ZY, Luo SS, Xie K, Cui GN, Sun MH (2013) Integrated intrusion detection model based on rough set and artificial immune. J Commun 34(9):166–176

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Shinly Swarna Sugi.

Ethics declarations

Conflict of interest

All author states that there is no conflict of interest.

Additional information

Communicated by V. Loia.

Publisher's Note

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

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-022-07781-7"

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.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sugi, S.S.S., Ratna, S.R. RETRACTED ARTICLE: A novel distributed training on fog node in IoT backbone networks for security. Soft Comput 24, 18399–18410 (2020). https://doi.org/10.1007/s00500-020-05047-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05047-8

Keywords

Navigation