Skip to main content
Erschienen in: Wireless Personal Communications 4/2020

13.04.2020

ELPC-Trust Framework for Wireless Sensor Networks

verfasst von: N. Dharini, N. Duraipandian, Jeevaa Katiravan

Erschienen in: Wireless Personal Communications | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Wireless Sensor Network (WSN) and its security concern play a vital part in its effecting functioning. WSN routing layer attacks pose a great threat to its functionality, whereas if a situation arises in which multiple attacks co-exist, then the scenario will become worse. In such cases the reliability of the network deployed is greatly questionable. Thus an Intrusion detection cum Trust based framework is mandatory to detect such co existing malicious activities in order to maintain the network reliability. Energy packed Cluster heads and sink nodes are utilized for this kind of trust computation process. To achieve this, an energy based abnormality detection of malicious nodes is proposed, upon which based on the packet rate and the lifetime of sensor nodes a trust value is computed. The trust framework involves two phases namely Threat Detection phase which performs attack categorization based on Energy, Packet Count and Z scores followed by the second phase which computes the trust values for each node. Trust scores are computed based on the intensity of the attack in terms of their network performance degradation. Attacks such as Gray hole, Black hole, Wormhole, Flooding and Data Modification attacks are addressed. Data Modification attacks are detected uniquely based on the Z scores values of the sensed data set and upon the location of the nodes. If any node is found to consume abnormal energy either below or above certain threshold, identified nodes are categorized into two groups based on their threshold. Hierarchical decision making is introduced, which implies only the abnormal energy consuming nodes (among the total sensor nodes deployed in the network) are considered for further packet count based attack categorization. This way we can reduce the computational overhead of traditional trust models in which each node is checked for its reliability against multiple trust factors. Upon detecting the attacks and computing the trust values of nodes, only the nodes which have a nominal trust value are included in the routing process. Simulations were carried out in NS2 Mannasim Framework with static and mobile sensor nodes. Proposed Energy-Lifetime-Packet Count (ELPC)-Trust framework was tested under five different types of attack scenarios in a co-existing manner. Performance of the network by including the proposed ELPC trust and by ignoring the detected malicious nodes increases the throughput by 500 and PDR by 40% when compared with the attack scenario. But on the other hand introduces a delay of 0.5 seconds. Network Performance is improved in case of static networks. Under mobile scenarios proposed ELPC trust incurs high amount of delay.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Literatur
1.
Zurück zum Zitat Messai, M. L. (2014). Classification of attacks in wireless sensor networks. International Congress on Telecommunication and Applications. Messai, M. L. (2014). Classification of attacks in wireless sensor networks. International Congress on Telecommunication and Applications.
3.
Zurück zum Zitat Dharini, N., Duraipandian, N., Katiravan, J. (2018). A novel IDS to detect multiple DoS attacks with network lifetime estimation based on learning-based energy prediction algorithm for hierarchical WSN. International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing. doi:10.1007/978-981-13-2182-5_1 Dharini, N., Duraipandian, N., Katiravan, J. (2018). A novel IDS to detect multiple DoS attacks with network lifetime estimation based on learning-based energy prediction algorithm for hierarchical WSN. International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing. doi:10.1007/978-981-13-2182-5_1
4.
Zurück zum Zitat Guangjie, H., Jinfang, J., Wen, S., Lei, S., & Joel, R. (2013). IDSEP: A novel intrusion detection scheme based on energy prediction in cluster-based wireless sensor networks. IET Information Security,7(2), 97–105.CrossRef Guangjie, H., Jinfang, J., Wen, S., Lei, S., & Joel, R. (2013). IDSEP: A novel intrusion detection scheme based on energy prediction in cluster-based wireless sensor networks. IET Information Security,7(2), 97–105.CrossRef
5.
Zurück zum Zitat Feng, R., Xu, X., Zhou, X., & Wan, J. (2011). A trust evaluation algorithm for wireless sensor networks based on node behaviours and D-S evidence theory. Sensors,11(2), 1345–1360.CrossRef Feng, R., Xu, X., Zhou, X., & Wan, J. (2011). A trust evaluation algorithm for wireless sensor networks based on node behaviours and D-S evidence theory. Sensors,11(2), 1345–1360.CrossRef
6.
Zurück zum Zitat Wu, R., Deng, X., Lu, R., Shen, X. (2012). Trust-based anomaly detection in wireless sensor networks. In Proceedings 1st IEEE International Conference on Communications in China, pp. 203–207. Wu, R., Deng, X., Lu, R., Shen, X. (2012). Trust-based anomaly detection in wireless sensor networks. In Proceedings 1st IEEE International Conference on Communications in China, pp. 203–207.
7.
Zurück zum Zitat Li, X., Zhou, F., & Du, J. (2013). LDTS: A lightweight and dependable trust system for clustered wireless sensor networks. IEEE Transactions on Information Forensics and Security,8(6), 924–935.CrossRef Li, X., Zhou, F., & Du, J. (2013). LDTS: A lightweight and dependable trust system for clustered wireless sensor networks. IEEE Transactions on Information Forensics and Security,8(6), 924–935.CrossRef
8.
Zurück zum Zitat Atakli, I, M., Hu, H., Chen, Y., Ku, W.S., Zhou, S. (2008). Malicious node detection in wireless sensor networks using weighted trust evaluation. In Proceedings of the 2008 Spring Simulation Multiconference, pp. 836–843. Atakli, I, M., Hu, H., Chen, Y., Ku, W.S., Zhou, S. (2008). Malicious node detection in wireless sensor networks using weighted trust evaluation. In Proceedings of the 2008 Spring Simulation Multiconference, pp. 836–843.
9.
Zurück zum Zitat Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M. (2015). An efficient distributed trust model for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,26(5), 1228–1237.CrossRef Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M. (2015). An efficient distributed trust model for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,26(5), 1228–1237.CrossRef
10.
Zurück zum Zitat Shaikh, R. A., Jameel, H., D’Auriol, B. J., Heejo Lee, H., Sungyoung Lee, S., & Song, Y. J. (2009). Group-based trust management scheme for clustered wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,20(11), 1698–1712.CrossRef Shaikh, R. A., Jameel, H., D’Auriol, B. J., Heejo Lee, H., Sungyoung Lee, S., & Song, Y. J. (2009). Group-based trust management scheme for clustered wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,20(11), 1698–1712.CrossRef
11.
Zurück zum Zitat Yao, Z., Kim, D., & Doh, Y. (2006) Plus: parameterized and localized trust management scheme for sensor networks security. In Proceedings of 2006 IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS), pp. 437–446. Yao, Z., Kim, D., & Doh, Y. (2006) Plus: parameterized and localized trust management scheme for sensor networks security. In Proceedings of 2006 IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS), pp. 437–446.
12.
Zurück zum Zitat Weizhi, M., Wenjuan, L., Chunhua, S., Jianying, Z., & Rongxing, L. (2017). Enhancing trust management for wireless intrusion detection via traffic sampling in the era of big data. IEEE Access,6, 7234–7243. Weizhi, M., Wenjuan, L., Chunhua, S., Jianying, Z., & Rongxing, L. (2017). Enhancing trust management for wireless intrusion detection via traffic sampling in the era of big data. IEEE Access,6, 7234–7243.
13.
Zurück zum Zitat Boyuan, S., & Donghui, L. (2018). A comprehensive trust aware routing with multi attributes for WSNs. IEEE Access,6, 4725–4821.CrossRef Boyuan, S., & Donghui, L. (2018). A comprehensive trust aware routing with multi attributes for WSNs. IEEE Access,6, 4725–4821.CrossRef
14.
Zurück zum Zitat Miglani, A., Bhatia, T., Sharma, G., Shrivastava, G. (2017). An energy efficient and trust aware framework for secure routing in LEACH for WSN: Scalable Computing. Practice and Experience: Special Issue on Secure Solutions for Networks in Mobile Scenarios. Scalable Computing, 13(3), 207–218 Miglani, A., Bhatia, T., Sharma, G., Shrivastava, G. (2017). An energy efficient and trust aware framework for secure routing in LEACH for WSN: Scalable Computing. Practice and Experience: Special Issue on Secure Solutions for Networks in Mobile Scenarios. Scalable Computing, 13(3), 207–218
15.
Zurück zum Zitat Zhenguo, C., Liqin, T., & Chuang, L. (2017). Trust model of wireless sensor networks and its application in data fusion. Sensors,17(4), 1–16.CrossRef Zhenguo, C., Liqin, T., & Chuang, L. (2017). Trust model of wireless sensor networks and its application in data fusion. Sensors,17(4), 1–16.CrossRef
16.
Zurück zum Zitat Amjad, M., Akbar, K., Muhammad, M. U., Salwani, A., & Khairul, A. Z. A. (2017). Secure knowledge and cluster based intrusion detection mechanism for smart wireless sensor networks. IEEE Access,6, 5688–5694. Amjad, M., Akbar, K., Muhammad, M. U., Salwani, A., & Khairul, A. Z. A. (2017). Secure knowledge and cluster based intrusion detection mechanism for smart wireless sensor networks. IEEE Access,6, 5688–5694.
17.
Zurück zum Zitat Vishwa, T. A., & Morgera, S. D. (2018). A multi-level intrusion detection system for wireless sensor networks based on immune theory. IEEE Access,6, 47364–47373.CrossRef Vishwa, T. A., & Morgera, S. D. (2018). A multi-level intrusion detection system for wireless sensor networks based on immune theory. IEEE Access,6, 47364–47373.CrossRef
18.
Zurück zum Zitat Katiravan, J., Duraipandian, N., & Dharini, N. (2015). A two level detection of routing layer attacks in hierarchical wireless sensor networks using learning based energy prediction. KSII Transactions on Internet and Information Systems,9(11), 4644–4661. Katiravan, J., Duraipandian, N., & Dharini, N. (2015). A two level detection of routing layer attacks in hierarchical wireless sensor networks using learning based energy prediction. KSII Transactions on Internet and Information Systems,9(11), 4644–4661.
23.
Zurück zum Zitat Buruhanudeen, S., Othman, M., & Ali, B. M. (2007) Mobility models, broadcasting methods on factors contributing towards the efficiency of the MANET routing protocols: overview. In Proceedings of IEEE fourteenth International Conference on Telecommunication and Eighth Malaysia International Conference on Communications, pp. 231–236. Buruhanudeen, S., Othman, M., & Ali, B. M. (2007) Mobility models, broadcasting methods on factors contributing towards the efficiency of the MANET routing protocols: overview. In Proceedings of IEEE fourteenth International Conference on Telecommunication and Eighth Malaysia International Conference on Communications, pp. 231–236.
24.
Zurück zum Zitat Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocols for wireless microsensor networks. Proceedings of the 33rd Hawaaian International Conference on Systems Science. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocols for wireless microsensor networks. Proceedings of the 33rd Hawaaian International Conference on Systems Science.
25.
Zurück zum Zitat Lindsey, S. & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. IEEE Aerospace Conference Proceedings, p. 26. Lindsey, S. & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. IEEE Aerospace Conference Proceedings, p. 26.
Metadaten
Titel
ELPC-Trust Framework for Wireless Sensor Networks
verfasst von
N. Dharini
N. Duraipandian
Jeevaa Katiravan
Publikationsdatum
13.04.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07288-0

Weitere Artikel der Ausgabe 4/2020

Wireless Personal Communications 4/2020 Zur Ausgabe

Neuer Inhalt