Skip to main content
Top
Published in: Cluster Computing 5/2019

01-02-2018

Wireless network confidence level improvement via fusion adaptive resonance theory

Authors: K. Chandraprabha, B. G. Geetha

Published in: Cluster Computing | Special Issue 5/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

One of the main hazards for the current internet community is the distributed denial of service attack (DDoS). Moreover, with the DDoS mechanisms being implicit in nature, the identification of it is very hard due to the unique characteristics of the normal traffic and useless packet sent to their victims by adversaries. The vulnerability of network mechanisms (VNM) accounts for maximal performance degradation on the system by the malicious users with the abundance resource availability. In addition, the Hash table based VNM suffers from longer waiting time with the attack size reflecting on the duplicate effect on the vulnerability. Rule-based firewall’s performance is evaluated by an analytical Queuing model that are exposed to standard traffic and DDoS attack flows where this model is based on embedded Markov chain (EMC). Queuing model based on EMC targets the different rule positions but mitigate DDoS attacks targeting bottom rules with minimal confidence in the network. To attain the confidence in the network, adaptive resonance theory (ART) is applied based on the semantic similarity measure. Furthermore, for learning inherent associations, a fusion method on the ART network is deployed. Fusion ART uses the multiple overlapping ART models. In this clusters are generated and associative mappings are coded transversely packet information in a real-time and continuous way. Semantic similarity category is combined in a fusion ART into predefined semantic categories to attain maximal confidence value in the network. ART scheme increases the trusted platform module by preventing the unnecessary DDoS attack. Fusion ART functionalities through ns2 simulation, produces the positive solution measured in terms of confidence rate in network, waiting time, CPU utilization, true positive rate, throughput.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

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!

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!

Literature
1.
go back to reference He, D., Bu, J., Chan, S., Yin, M.: Privacy-preserving universal authentication protocol for wireless communications. IEEE Trans. Wirel. Commun. 10(2), 431–436 (2011)CrossRef He, D., Bu, J., Chan, S., Yin, M.: Privacy-preserving universal authentication protocol for wireless communications. IEEE Trans. Wirel. Commun. 10(2), 431–436 (2011)CrossRef
2.
go back to reference Yu, S., Zhou, W., Doss, R., Jia, W.: Traceback of DDoS attacks using entropy variations. IEEE Trans. Distrib. Syst. 22(3), 412–425 (2011)CrossRef Yu, S., Zhou, W., Doss, R., Jia, W.: Traceback of DDoS attacks using entropy variations. IEEE Trans. Distrib. Syst. 22(3), 412–425 (2011)CrossRef
3.
go back to reference Yu, S., Zhou, W., Jia, W., Guo, S.: Discriminating DDoS attacks from flash crowds using flow correlation coefficient. IEEE Trans. Distrib. Syst. 23(6), 1073–1080 (2012)CrossRef Yu, S., Zhou, W., Jia, W., Guo, S.: Discriminating DDoS attacks from flash crowds using flow correlation coefficient. IEEE Trans. Distrib. Syst. 23(6), 1073–1080 (2012)CrossRef
4.
go back to reference Khaled, Salah, Khalid, Elbadawi, Raouf, Boutaba: Performance modeling and analysis of network firewalls. IEEE Trans. Netw. Serv. Manag. 9(1), 12–21 (2012)CrossRef Khaled, Salah, Khalid, Elbadawi, Raouf, Boutaba: Performance modeling and analysis of network firewalls. IEEE Trans. Netw. Serv. Manag. 9(1), 12–21 (2012)CrossRef
5.
go back to reference Ben-Porat, U., Bremler-Barr, A., Levy, H.: Vulnerability of network mechanisms to sophisticated DDoS Attacks. IEEE Trans. Comput. 62(5), 1031–1043 (2013)MathSciNetCrossRef Ben-Porat, U., Bremler-Barr, A., Levy, H.: Vulnerability of network mechanisms to sophisticated DDoS Attacks. IEEE Trans. Comput. 62(5), 1031–1043 (2013)MathSciNetCrossRef
6.
go back to reference Kwon, T., Lee, J., Song, J.: Location-based pairwise key predistribution for wireless sensor networks. IEEE Trans. Wirel. Commun. 8, 11 (2009) Kwon, T., Lee, J., Song, J.: Location-based pairwise key predistribution for wireless sensor networks. IEEE Trans. Wirel. Commun. 8, 11 (2009)
7.
go back to reference Li, Y.Y., Parker, L.E.: Detecting and monitoring time-related abnormal events using a wireless sensor network and mobile robot. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Nice, France, (2008) Li, Y.Y., Parker, L.E.: Detecting and monitoring time-related abnormal events using a wireless sensor network and mobile robot. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Nice, France, (2008)
8.
go back to reference Paschalidis, I.C., Smaragdakis, G.: Spatio-temporal network anomaly detection by assessing deviations of empirical measures. IEEE/ACM Trans. Netw. 17(3), 685–697 (2009)CrossRef Paschalidis, I.C., Smaragdakis, G.: Spatio-temporal network anomaly detection by assessing deviations of empirical measures. IEEE/ACM Trans. Netw. 17(3), 685–697 (2009)CrossRef
9.
go back to reference Anastasopoulos, M.P., Arapoglou, P.D.M., Kannan, R., Cottis, P.G.: Adaptive routing strategies in IEEE 802.16 multi-hop wireless backhaul networks based on evolutionary game theory. IEEE J. Commun. 26, 7 (2008) Anastasopoulos, M.P., Arapoglou, P.D.M., Kannan, R., Cottis, P.G.: Adaptive routing strategies in IEEE 802.16 multi-hop wireless backhaul networks based on evolutionary game theory. IEEE J. Commun. 26, 7 (2008)
10.
go back to reference Li, Y.Y., Thomason, M., Parker, L.E.: Detecting time-related changes in wireless sensor networks using symbol compression and Probabilistic Suffix Trees. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, (2010) Li, Y.Y., Thomason, M., Parker, L.E.: Detecting time-related changes in wireless sensor networks using symbol compression and Probabilistic Suffix Trees. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, (2010)
11.
go back to reference Xu, W., Zhang, X., Hu, H., Ahn, G.J., Seifert, J.P.: Remote attestation with domain-based integrity model and policy analysis. IEEE Trans. Dependable Secur. Comput. 9(3), 429–442 (2012)CrossRef Xu, W., Zhang, X., Hu, H., Ahn, G.J., Seifert, J.P.: Remote attestation with domain-based integrity model and policy analysis. IEEE Trans. Dependable Secur. Comput. 9(3), 429–442 (2012)CrossRef
12.
go back to reference Hu, H., Ahn, G.J., Kulkarni, K.: Detecting and resolving firewall policy anomalies. IEEE Trans. Dependable Secur. Comput. 9(3), 318–331 (2012)CrossRef Hu, H., Ahn, G.J., Kulkarni, K.: Detecting and resolving firewall policy anomalies. IEEE Trans. Dependable Secur. Comput. 9(3), 318–331 (2012)CrossRef
13.
go back to reference Gianvecchio, S., Xie, M., Wu, Z., Wang, H.: Humans and bots in internet chat: measurement, analysis, and automated classification. IEEE/ACM Trans. Netw. 19(5), 1557–1571 (2011)CrossRef Gianvecchio, S., Xie, M., Wu, Z., Wang, H.: Humans and bots in internet chat: measurement, analysis, and automated classification. IEEE/ACM Trans. Netw. 19(5), 1557–1571 (2011)CrossRef
14.
go back to reference Chin, J.C., Dong, Y., Hon, W.K., Ma, C.Y.T., Yau, D.K.: Detection of intelligent mobile target in a mobile sensor network. IEEE/ACM Trans. Netw. 18(1), 41–52 (2010)CrossRef Chin, J.C., Dong, Y., Hon, W.K., Ma, C.Y.T., Yau, D.K.: Detection of intelligent mobile target in a mobile sensor network. IEEE/ACM Trans. Netw. 18(1), 41–52 (2010)CrossRef
15.
go back to reference Kulakov, A., Davcev, D.: Tracking of unusual events in wireless sensor networks based on artificial neural-network algorithms. Inf. Technol. 2, 534–539 (2005) Kulakov, A., Davcev, D.: Tracking of unusual events in wireless sensor networks based on artificial neural-network algorithms. Inf. Technol. 2, 534–539 (2005)
16.
go back to reference Li, Y., Parker, L.E.: A spatial-temporal imputation technique for classification with missing data in a wireless sensor network. In: Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems (IROS), Nice, France, (2008) Li, Y., Parker, L.E.: A spatial-temporal imputation technique for classification with missing data in a wireless sensor network. In: Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems (IROS), Nice, France, (2008)
Metadata
Title
Wireless network confidence level improvement via fusion adaptive resonance theory
Authors
K. Chandraprabha
B. G. Geetha
Publication date
01-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 5/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1606-9

Other articles of this Special Issue 5/2019

Cluster Computing 5/2019 Go to the issue

Premium Partner