Skip to main content
SpringerLink
Log in

An intruder defense model for the detection of power grid disturbances in wireless network

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Cyber security has to gain a high level of awareness in the Network and Computer pasture due to the large spread of information transmission technology. A powerful False Data Injection (FDI) Intruder monitors the network activities and injects the malicious data thereby causing failure in the power system. To overcome this defense, the “Conviction based Intruder Defense Model” is proposed to identify and isolate it from the network by providing secure transmission. This scheme operates in three phases. In the first phase, the data are analyzed with the library files to identify the conviction values. Based on the conviction values the resulting factors are analyzed with different iterations and the suspicious drafts are identified and classified using Fuzzy Intrusion Detection System (FIDS) divider. In the second phase, three algorithms are used to organize the drafts categorized. In the third phase, abnormal nodes are isolated from the network. Experimental results show higher accuracy and detection rates with low false positives.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Abbreviations

\( S \) :

source

\( D \) :

destination

\( \varGamma h^{\tau } \) :

accuracy

\( \delta m^{r} \) :

detection rate

\( \xi^{hn} \) :

false measure

\( \varPi_{t}^{S,D} \) :

library files

\( I_{L} \) :

intruder

\( V_{(s,h)} \) :

conviction table

\( \{ X_{1} ,X_{2} ,X_{3} ,X_{4} \} \) :

sectors

\( O_{1} ,O_{2} ,O_{3} ,O_{4} ,O_{5} \) :

output path

\( \lambda_{1} ,\lambda_{2} \ldots \ldots \lambda_{K} \) :

intermediate node

\( \tau \) :

threshold

\( W_{K} \) :

weight

References

  1. Kim S and Park S 2017 CPS based manufacturing system optimization. Procedia Comput. Sci. 122:518–24

    Article  Google Scholar 

  2. Wei R, Kelly T P, Hawkins R and Armengaud E 2018 Deis: Dependability engineering innovation for cyber-physical systems. Federation of International Conferences on software technologies: applications and foundations. 10748:409–16

    Google Scholar 

  3. Irmak E and Erkek ˙I 2018 An overview of cyber-attack vectors on scada systems. In: Digital Forensic and Security (ISDFS), IEEE, pp. 1–5

  4. Spuhler M, Giustiniano D, Lenders V, Wilhelm M and Schmitt J B 2014 Detection of reactive jamming in DSSS. Wireless Communications 13(3): 1593–1603

    Google Scholar 

  5. Strasser M, Danev B and Capkun S (2012) Detection of reactive jamming. Sensor Networks 7(2): 1–29

    Google Scholar 

  6. Richa A, Scheideler C, Schmid S and Zhang J 2018 An Efficient and Fair MAC protocol Robust to Reactive Interference. IEEE/ACM Transaction on Networking 21(3): 760–771

    Article  Google Scholar 

  7. Xuan Y, Shen Nguyen N P and Thai M T 2011 A Trigger Identification Service for Defending Reactive Jammers in WSN. IEEE Transaction on Mobile Computing 11(5):793–806

    Article  Google Scholar 

  8. Zhan, Liu X, Shuai Z, Li Z and Wen Y 2018 Cyber cascades screening considering the impacts of false data injection attacks. IEEE Trans. Power Syst. 33(6):6545–6556

    Article  Google Scholar 

  9. Chen Maglaras 2018 Stuxnet worm impact on industrial cyber-physical system security. In: IEEE, pp. 4490–4494

  10. Shiu A and Sastry S 2017 A taxonomy of cyber attacks on SCADA systems. In: IEEE, pp. 380–388.

  11. Ahmed C M, Zhou J and Mathur A P 2018 Noise matters: Using sensor and process noise fingerprint to detect stealthy cyber attacks and authenticate sensors in cps. In: ACM, pp. 566–581

  12. Shoukry 2017. Analysis of the cyber attack on the ukrainian power grid. In: Electric. Inform. Shar. Anal. Center

  13. Hadziosmanovic, Tomin N V, Kurbatsky V G, Sidorov D Nand Zhukov A V 2017. Machine learning techniques for power system security assessment. IFAC-Papers OnLine 49(27):445–50

    Google Scholar 

  14. Junejo, Honeine P, Beauseroy P 2018 Lp-norms in one-class classification for intru- sion detection in SCADA systems. IEEE Trans. Ind. Informa. 10(4):2308–17

    Google Scholar 

  15. Nader P, Honeine P, Beauseroy P 2014 Mahalanobis-based one-class classification. Machine learning for signal processing. In: IEEE, pp. 1–6

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India

    R B Benisha & S Raja Ratna

Authors
  1. R B Benisha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S Raja Ratna
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R B Benisha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benisha, R.B., Raja Ratna, S. An intruder defense model for the detection of power grid disturbances in wireless network. Sādhanā 45, 154 (2020). https://doi.org/10.1007/s12046-020-01404-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-020-01404-3

Keywords

Search

Navigation