Skip to main content
Top
Published in: Wireless Personal Communications 3/2017

06-09-2016

A Secure Group-Based Blackhole Node Detection Scheme for Hierarchical Wireless Sensor Networks

Authors: Mohammad Wazid, Ashok Kumar Das

Published in: Wireless Personal Communications | Issue 3/2017

Log in

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

search-config
loading …

Abstract

Rapid development of the wireless communication technology and low cost of sensing devices has accelerated the development of wireless sensor networks (WSNs). These types of networks have a wide range of applications including habitat monitoring, health monitoring, data acquisition in hazardous environmental conditions and military operations. Sensor nodes are resource constrained having limited communication range, battery and processing power. Sensor nodes are prone to failure and can be also physically captured by an adversary. One of the main concerns in WSNs is to provide security, especially in the cases where they are deployed for military applications and monitoring. Further, WSNs are prone to various attacks such as wormhole, sinkhole and blackhole attacks. A blackhole attack is a kind of denial of service attack, which is very difficult to detect and defend and such blackhole attack, if happens, affects the entire performance of the network. In addition, it causes high end-to-end delay and less throughput with less packet delivery ratio. The situation can be worst if multiple blackhole attacker nodes present in the network. As a result, detection and prevention of the blackhole attack becomes crucial in WSNs. In this paper, we aim to propose a new efficient group-based technique for the detection and prevention of multiple blackhole attacker nodes in WSNs. In our approach, the entire WSN is divided into several clusters and each cluster has a powerful high-end sensor node (called a cluster head), which is responsible for the detection of blackhole attacker nodes, if present, in that cluster. The proposed scheme achieves about 90 % detection rate and 3.75 % false positive rate, which are significantly better than the existing related schemes. Furthermore, our scheme is efficient and thus, it is very appropriate for practical applications in WSNs.

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

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+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 "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 Das, A. K., Sharma, P., Chatterjee, S., & Sing, J. K. (2012). A dynamic password-based user authentication scheme for hierarchical wireless sensor networks. Journal of Network and Computer Applications, 35(5), 1646–1656.CrossRef Das, A. K., Sharma, P., Chatterjee, S., & Sing, J. K. (2012). A dynamic password-based user authentication scheme for hierarchical wireless sensor networks. Journal of Network and Computer Applications, 35(5), 1646–1656.CrossRef
2.
go back to reference Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef
3.
go back to reference Cheng, Y., & Agrawal, D. P. (2007). An improved key distribution mechanism for large-scale hierarchical wireless sensor networks. Ad Hoc Networks, 5(1), 35–48.CrossRef Cheng, Y., & Agrawal, D. P. (2007). An improved key distribution mechanism for large-scale hierarchical wireless sensor networks. Ad Hoc Networks, 5(1), 35–48.CrossRef
4.
go back to reference Dong, D., Li, M., Liu, Y., Li, X., & Liao, X. (2011). Topological detection on wormholes in wireless ad hoc and sensor networks. IEEE/ACM Transactions on Networking, 19(6), 1787–1796.CrossRef Dong, D., Li, M., Liu, Y., Li, X., & Liao, X. (2011). Topological detection on wormholes in wireless ad hoc and sensor networks. IEEE/ACM Transactions on Networking, 19(6), 1787–1796.CrossRef
5.
go back to reference Shafieia, H., Khonsaria, A., Derakhshia, H., & Mousavia, P. (2014). Detection and mitigation of sinkhole attacks in wireless sensor networks. Computer and System Sciences, 80(3), 644–653.CrossRef Shafieia, H., Khonsaria, A., Derakhshia, H., & Mousavia, P. (2014). Detection and mitigation of sinkhole attacks in wireless sensor networks. Computer and System Sciences, 80(3), 644–653.CrossRef
6.
go back to reference Chatterjee, S., & Das, A. K. (2015). An effective ECC-based user access control scheme with attribute-based encryption for wireless sensor networks. Security and Communication Networks, 8(9), 1752–1771.CrossRef Chatterjee, S., & Das, A. K. (2015). An effective ECC-based user access control scheme with attribute-based encryption for wireless sensor networks. Security and Communication Networks, 8(9), 1752–1771.CrossRef
7.
go back to reference Das, A. K. (2016). A secure and robust temporal credential-based three-factor user authentication scheme for wireless sensor networks. Peer-to-Peer Networking and Applications, 9(1), 223–244.CrossRef Das, A. K. (2016). A secure and robust temporal credential-based three-factor user authentication scheme for wireless sensor networks. Peer-to-Peer Networking and Applications, 9(1), 223–244.CrossRef
8.
go back to reference Das, A. K. (2015). A secure and effective biometric-based user authentication scheme for wireless sensor networks using smart card and fuzzy extractor. International Journal of Communication Systems. doi:10.1002/dac.2933. Das, A. K. (2015). A secure and effective biometric-based user authentication scheme for wireless sensor networks using smart card and fuzzy extractor. International Journal of Communication Systems. doi:10.​1002/​dac.​2933.
9.
go back to reference Das, A. K. (2015). A secure and efficient user anonymity-preserving three-factor authentication protocol for large-scale distributed wireless sensor networks. Wireless Personal Communications, 82(3), 1377–1404.CrossRef Das, A. K. (2015). A secure and efficient user anonymity-preserving three-factor authentication protocol for large-scale distributed wireless sensor networks. Wireless Personal Communications, 82(3), 1377–1404.CrossRef
10.
go back to reference Dolev, D., & Yao, A. C. (1983). On the security of public key protocols. IEEE Transactions on Information Theory, 29(2), 198–208.MathSciNetCrossRefMATH Dolev, D., & Yao, A. C. (1983). On the security of public key protocols. IEEE Transactions on Information Theory, 29(2), 198–208.MathSciNetCrossRefMATH
11.
go back to reference Das, M. L. (2009). Two-factor user authentication in wireless sensor networks. IEEE Transactions on Wireless Communications, 8(3), 1086–1090.MathSciNetCrossRef Das, M. L. (2009). Two-factor user authentication in wireless sensor networks. IEEE Transactions on Wireless Communications, 8(3), 1086–1090.MathSciNetCrossRef
12.
go back to reference Li, Y. Y., Li, K., Zhou, W., & Li, P. (2012). Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures. Journal of Network and Computer Applications, 35(3), 867–880.CrossRef Li, Y. Y., Li, K., Zhou, W., & Li, P. (2012). Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures. Journal of Network and Computer Applications, 35(3), 867–880.CrossRef
13.
go back to reference Wazid, M., Katal, A., Sachan, R. S., Goudar, R. H., & Singh, D. P. (2013). Detection and prevention mechanism for blackhole attack in wireless sensor network. In IEEE international conference on communication and signal processing (ICCSP) (pp. 576–581), Melmaruvathur, India. Wazid, M., Katal, A., Sachan, R. S., Goudar, R. H., & Singh, D. P. (2013). Detection and prevention mechanism for blackhole attack in wireless sensor network. In IEEE international conference on communication and signal processing (ICCSP) (pp. 576–581), Melmaruvathur, India.
14.
go back to reference Guechari, M., Mokdad, L., & Tan, S. (2012). Dynamic solution for detecting Denial of Service attacks. In IEEE international conference on communications (pp. 173–177), Ottawa, Canada. Guechari, M., Mokdad, L., & Tan, S. (2012). Dynamic solution for detecting Denial of Service attacks. In IEEE international conference on communications (pp. 173–177), Ottawa, Canada.
15.
go back to reference Misra, S., Bhattarai, K., & Xue, G. (2011). BAMBi: Blackhole attacks mitigation with multiple base stations in wireless sensor networks. In IEEE international conference on communications (ICC) (pp. 1–5), Kyoto, Japan. Misra, S., Bhattarai, K., & Xue, G. (2011). BAMBi: Blackhole attacks mitigation with multiple base stations in wireless sensor networks. In IEEE international conference on communications (ICC) (pp. 1–5), Kyoto, Japan.
16.
go back to reference Wang, Y., Fu, W., & Agrawal, D. P. (2013). Gaussian versus uniform distribution for intrusion detection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(2), 342–355.CrossRef Wang, Y., Fu, W., & Agrawal, D. P. (2013). Gaussian versus uniform distribution for intrusion detection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(2), 342–355.CrossRef
17.
go back to reference Wang, Y., Wang, X., Xie, B., Wang, D., & Agrawal, D. P. (2008). Intrusion detection in homogeneous and heterogeneous wireless sensor networks. IEEE Transactions on Mobile Computing, 7(6), 698–711.CrossRef Wang, Y., Wang, X., Xie, B., Wang, D., & Agrawal, D. P. (2008). Intrusion detection in homogeneous and heterogeneous wireless sensor networks. IEEE Transactions on Mobile Computing, 7(6), 698–711.CrossRef
18.
go back to reference Li, W., Yi, P., Wu, Y., Pan, L., & Li, J. (2014). A new intrusion detection system based on KNN classification algorithm in wireless sensor network. Electrical and computer engineering, 2014:1–8. Article ID 240217. doi:10.1155/2014/240217. Li, W., Yi, P., Wu, Y., Pan, L., & Li, J. (2014). A new intrusion detection system based on KNN classification algorithm in wireless sensor network. Electrical and computer engineering, 2014:1–8. Article ID 240217. doi:10.​1155/​2014/​240217.
19.
go back to reference Xie, M., Hu, J., Han, S., & Chen, H. (2013). Scalable hyper grid k-NN-based online anomaly detection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(8), 1661–1670.CrossRef Xie, M., Hu, J., Han, S., & Chen, H. (2013). Scalable hyper grid k-NN-based online anomaly detection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(8), 1661–1670.CrossRef
20.
go back to reference Shin, S., Kwon, T., Jo, G., Park, Y., & Rhy, H. (2010). An experimental study of hierarchical intrusion detection for wireless industrial sensor networks. IEEE Transactions on Industrial Informatics, 6(4), 744–757.CrossRef Shin, S., Kwon, T., Jo, G., Park, Y., & Rhy, H. (2010). An experimental study of hierarchical intrusion detection for wireless industrial sensor networks. IEEE Transactions on Industrial Informatics, 6(4), 744–757.CrossRef
21.
go back to reference Rajasegarar, S., Leckie, C., Bezdek, J. C., & Palaniswami, M. (2010). Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Transactions on Information Forensics and Security, 5(3), 518–533.CrossRef Rajasegarar, S., Leckie, C., Bezdek, J. C., & Palaniswami, M. (2010). Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Transactions on Information Forensics and Security, 5(3), 518–533.CrossRef
22.
go back to reference Abduvaliyev, A., Pathan, A. S. K., Zhou, Z., Roman, R., & Wong, W. (2013). On the vital areas of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 15(3), 1223–1237.CrossRef Abduvaliyev, A., Pathan, A. S. K., Zhou, Z., Roman, R., & Wong, W. (2013). On the vital areas of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 15(3), 1223–1237.CrossRef
23.
go back to reference Su, M. (2011). Using clustering to improve the KNN-based classifiers for online anomaly network traffic identification. Journal of Network and Computer Applications, 34(2), 722–730.CrossRef Su, M. (2011). Using clustering to improve the KNN-based classifiers for online anomaly network traffic identification. Journal of Network and Computer Applications, 34(2), 722–730.CrossRef
24.
go back to reference Wang, F., & Jiangchuan, L. (2011). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials, 13(4), 673–687.CrossRef Wang, F., & Jiangchuan, L. (2011). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials, 13(4), 673–687.CrossRef
25.
go back to reference Modares, H., Salleh, R., & Moravejosharieh, A. (2011). Overview of security issues in wireless sensor networks. IEEE international conference on computational intelligence modelling & simulation (pp. 53–57), Langkawi, Malaysia. Modares, H., Salleh, R., & Moravejosharieh, A. (2011). Overview of security issues in wireless sensor networks. IEEE international conference on computational intelligence modelling & simulation (pp. 53–57), Langkawi, Malaysia.
26.
go back to reference Prathapani, A., Santhanam, L., & Agrawal, D. P. (2009). Intelligent honeypot agent for blackhole attack detection in wireless mesh networks. In IEEE 6th international conference on mobile adhoc and sensor system (pp. 753–758), Macau, China. Prathapani, A., Santhanam, L., & Agrawal, D. P. (2009). Intelligent honeypot agent for blackhole attack detection in wireless mesh networks. In IEEE 6th international conference on mobile adhoc and sensor system (pp. 753–758), Macau, China.
27.
go back to reference Tiwari, M., Arya, K. V., Choudhari, R., & Choudhary, S. K. (2009). Designing intrusion detection to detect black hole and selective forwarding attack in WSN based on local information. In IEEE 4th international conference on computer sciences and convergence information technology (pp. 824–828), Seoul, South Korea. Tiwari, M., Arya, K. V., Choudhari, R., & Choudhary, S. K. (2009). Designing intrusion detection to detect black hole and selective forwarding attack in WSN based on local information. In IEEE 4th international conference on computer sciences and convergence information technology (pp. 824–828), Seoul, South Korea.
28.
go back to reference Raymond, D. R., & Midkiff, S. F. (2008). Network traffic classification using correlation information. IEEE Transactions on Parallel and Distributed Systems, 7(1), 74–81. Raymond, D. R., & Midkiff, S. F. (2008). Network traffic classification using correlation information. IEEE Transactions on Parallel and Distributed Systems, 7(1), 74–81.
29.
go back to reference Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In 33rd Hawaii international conference on system sciences (pp. 1–10), Hawaii, USA. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In 33rd Hawaii international conference on system sciences (pp. 1–10), Hawaii, USA.
30.
go back to reference Gao, H., Wu, R., Cao, M., & Zhang, C. (2014). Detection and defense technology of blackhole attacks in wireless sensor network. 14th International conference on algorithms and architectures for parallel processing (ICA3PP 2014), Lecture notes in computer science (Vol. 8631, pp. 601–610), Dalian, China. Gao, H., Wu, R., Cao, M., & Zhang, C. (2014). Detection and defense technology of blackhole attacks in wireless sensor network. 14th International conference on algorithms and architectures for parallel processing (ICA3PP 2014), Lecture notes in computer science (Vol. 8631, pp. 601–610), Dalian, China.
31.
go back to reference Das, A. K. (2009). An unconditionally secure key management scheme for large-scale heterogeneous wireless sensor networks. In First international on communication systems and networks and workshops (COMSNETS 2009) (pp. 1–10), Bangalore, India, IEEE. Das, A. K. (2009). An unconditionally secure key management scheme for large-scale heterogeneous wireless sensor networks. In First international on communication systems and networks and workshops (COMSNETS 2009) (pp. 1–10), Bangalore, India, IEEE.
32.
go back to reference Liu, D., Ning, P., Liu, A., Wang, C., & Du, W . K. (2008). Attack-resistant location estimation in wireless sensor networks. ACM Transactions on Information and System Security, 11(4), 22:1–22:39.CrossRef Liu, D., Ning, P., Liu, A., Wang, C., & Du, W . K. (2008). Attack-resistant location estimation in wireless sensor networks. ACM Transactions on Information and System Security, 11(4), 22:1–22:39.CrossRef
33.
go back to reference Ghazvini, M., Vahabi, M., Rasid, M., Abdullah, R., & Musa, W. (2008). Low energy consumption MAC protocol for wireless sensor networks. In IEEE 2nd international conference on sensor technologies and applications (pp. 49–54), Cap Esterel, France. Ghazvini, M., Vahabi, M., Rasid, M., Abdullah, R., & Musa, W. (2008). Low energy consumption MAC protocol for wireless sensor networks. In IEEE 2nd international conference on sensor technologies and applications (pp. 49–54), Cap Esterel, France.
34.
go back to reference Park, S., Hong, S. W., Lee c, E., Kim, S. H., & Crespi, N. (2015). Large-scale mobile phenomena monitoring with energy-efficiency in wireless sensor networks. Computer Networks, 81, 116–135.CrossRef Park, S., Hong, S. W., Lee c, E., Kim, S. H., & Crespi, N. (2015). Large-scale mobile phenomena monitoring with energy-efficiency in wireless sensor networks. Computer Networks, 81, 116–135.CrossRef
36.
go back to reference The Network Simulator-ns-2. http://http://www.isi.edu/nsnam/ns/. Accessed on March 2015. The Network Simulator-ns-2. http://​http://www.isi.edu/nsnam/ns/. Accessed on March 2015.
38.
go back to reference Hubballi, N., Biswas, S., & Nandi, S. (2011). Network specific false alarm reduction in intrusion detection system. Security and Communication Networks, 4(11), 1339–1349.CrossRef Hubballi, N., Biswas, S., & Nandi, S. (2011). Network specific false alarm reduction in intrusion detection system. Security and Communication Networks, 4(11), 1339–1349.CrossRef
39.
go back to reference Hubballi, N., Biswas, S., & Nandi, S. (2013). Towards reducing false alarms in network intrusion detection systems with data summarization technique. Security and Communication Networks, 6(3), 275–285.CrossRef Hubballi, N., Biswas, S., & Nandi, S. (2013). Towards reducing false alarms in network intrusion detection systems with data summarization technique. Security and Communication Networks, 6(3), 275–285.CrossRef
40.
go back to reference Kasliwal, B., Bhatia, S., Saini, S., Thaseen, I. S., & Kumar, C. A. (2014). A hybrid anomaly detection model using G-LDA. In IEEE international advance computing conference (IACC) (pp. 288–293), Gurgaon, India. Kasliwal, B., Bhatia, S., Saini, S., Thaseen, I. S., & Kumar, C. A. (2014). A hybrid anomaly detection model using G-LDA. In IEEE international advance computing conference (IACC) (pp. 288–293), Gurgaon, India.
Metadata
Title
A Secure Group-Based Blackhole Node Detection Scheme for Hierarchical Wireless Sensor Networks
Authors
Mohammad Wazid
Ashok Kumar Das
Publication date
06-09-2016
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 3/2017
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-016-3676-z

Other articles of this Issue 3/2017

Wireless Personal Communications 3/2017 Go to the issue