Reduce Effect of Dependent Malicious Sensor Nodes in WSNs using Pairs Counting and Fake Packets

Abstract

In this paper, we propose a new technique for the enhancement of target detection in Wireless Sensor Networks (WSNs) in which sensor nodes are responsible for taking binary decisions about the presence or absence of a given target and reporting the output to the fusion center. We introduce the algorithm; Fail Silent Pair (FSP) to calculate global decision in the fusion center. The FSP algorithm randomly distributes all sensor nodes into pairs then considers pairs of the same local decision. Also, we present new detection and prevention methods to reduce the effect of dependent malicious sensor nodes. The detection method is based on the deception of suspicious sensor nodes with fake packets to detect a subset of the malicious sensor nodes, as these nodes eavesdrop on other sensor nodes packets to use their local decisions as a reference to build an intelligent decision. While the prevention method allows the fusion center to correct local decisions of some malicious sensor nodes with identified strategies, assisting in the increase of the accuracy of global decisions. We introduce a mathematical analysis to verify our methods, in addition to simulation experiments to validate our technique.

Author Biographies

Ashraf Ahmad, Princess Sumya University for Technology
Ashraf Ahmad is currently the dean of computing sciences at Princess Sumya University for Technology (PSUT). Dr. Ahmad has obtained his PhD degree in Computer Science and Engineering from National Chiao Tung University (NCTU) in Taiwan with distinction. He obtained his B.Sc. degree from PSUT in Jordan. Dr. Ahmad has been the chairman of computer graphics and animation department in PSUT for four years. His interest area includes security application development and computer programming, mobile application, video transcoding, secure multimedia and interoperability. Dr. Ahmad has authored several scientific publications including journal papers, conference papers, book chapters and book. In addition, Dr. Ahmad has several US and international patents in his field of expertise. He serves as an associate editor in two journals and editorial member of several journals. Ashraf has been honored to serve as either a program committee or steering committee member for several international conferences. He has several funded projects on security and video domain. Moreover, Dr. Ahmad gave several Keynote speeches at many international conferences and workshops. With his supervision, his student teams won several international, regional and national prizes including imagine cup and world championship in programing. He has earned several funded research projects. Ashraf Ahmad has been chosen as one of the recipients of Leading Scientist award in the year 2006 for his outstanding contribution in the field of Video Processing and Communications. In 2014, Dr. Ahmad won the American challenge cup in Jordan for his invention border security device.
Mohammed Hababeh
Computer Engineering, German Jordanian UniversityAmman Madaba Street, P.O. Box 35247, Jordan
Alaa Abu-Hantash
University of Science and Technology, Irbid, JordanP.O.Box 3030, Irbid 22110, Jordan
Yousef AbuHour
University of Science and Technology, Irbid, JordanP.O.Box 3030, Irbid 22110, Jordan
Husam Musleh
University of Science and Technology, Irbid, JordanP.O.Box 3030, Irbid 22110, Jordan

References

[1] Althunibat, S.; Antonopoulos, A.; Kartsakli, E.; Granelli, F.; Verikoukis, C. (2016). Countering intelligent-dependent malicious nodes in target detection wireless sensor networks. IEEE Sensors Journal, 16(23), 8627-8639, 2016.
https://doi.org/10.1109/JSEN.2016.2606759

[2] Antonopoulos, A.; Verikoukis, C. (2016). Misbehavior detection in the internet of things: A network-coding-aware statistical approach. In Industrial Informatics (INDIN), 2016 IEEE 14th International Conference on, 1024-1027. IEEE, 2016.
https://doi.org/10.1109/INDIN.2016.7819313

[3] Anwar, R. W.; Bakhtiari, M.; Zainal, A.; Abdullah, A. H.; Qureshi, K. N.; Computing, F.; Bahru, J. (2014). Security issues and attacks in wireless sensor network. World Applied Sciences Journal, 30(10):1224-1227, 2014.

[4] Buratti, C.; Conti, A.; Dardari, D.; Verdone, R. (2009). An overview on wireless sensor networks technology and evolution. Sensors, 9(9), 6869-6896, 2009.
https://doi.org/10.3390/s90906869

[5] Curiac, D.-I.; Banias, O.; Dragan, F.; Volosencu, C.; Dranga, O. (2007). Malicious node detection in wireless sensor networks using an autoregression technique. In Networking and Services, 2007. ICNS. Third International Conference on, IEEE, 83-83, 2007.
https://doi.org/10.1109/ICNS.2007.79

[6] Dâmaso, A.; Freitas, D.; Rosa, N.; Silva, B.; Maciel, P. (2013). Evaluating the power consumption of wireless sensor network applications using models. Sensors, 13(3), 3473-3500, 2013.
https://doi.org/10.3390/s130303473

[7] Demirbas, M. (2005). Wireless sensor networks for monitoring of large public buildings, 2005.

[8] Di Pietro, R.; Mancini, L. V.; Soriente, C.; Spognardi, A.; Tsudik, G. (2008). Catch me (if you can): Data survival in unattended sensor networks. In Pervasive Computing and Communications, 2008. PerCom 2008. Sixth Annual IEEE International Conference on, IEEE, 185-194, 2008.
https://doi.org/10.1109/PERCOM.2008.31

[9] Hiregoudar, S.; Manjunath, K. (2017). Effective malicious node detection and data fusion under byzantine attacks, 2017.

[10] Kim, T.; Kim, I. H.; Sun, Y.; Jin, Z. (2015). Physical layer and medium access control design in energy efficient sensor networks: An overview. IEEE Transactions on Industrial Informatics, 11(1), 2-15, 2015.
https://doi.org/10.1109/TII.2014.2379511

[11] Lara, R.; Benítez, D.; Caamaño, A.; Zennaro, M.; Rojo-Álvarez, J. L. (2015). On real-time performance evaluation of volcano-monitoring systems with wireless sensor networks. IEEE Sensors Journal, 15(6), 3514-3523, 2015.
https://doi.org/10.1109/JSEN.2015.2393713

[12] Li, J.; Andrew, L. L.; Foh, C. H.; Zukerman, M.; Chen, H.-H. (2009). Connectivity, coverage and placement in wireless sensor networks. Sensors, 9(10), 7664-7693, 2009.
https://doi.org/10.3390/s91007664

[13] Pires, W.; de Paula Figueiredo, T. H.; Wong, H. C.; Loureiro, A. A. F. (2004). Malicious node detection in wireless sensor networks. In Parallel and distributed processing symposium, 2004. Proceedings. 18th international, page 24. IEEE, 2004.
https://doi.org/10.1109/IPDPS.2004.1302934

[14] Plastoi, M.; Volosencu, C.; Banias, O.; Tudoroiu, R.; Curiac, D.-I.; Doboli, A. (2009). Integrated system for malicious node discovery and self-destruction in wireless sensor networks. International Journal on Advances in Networks and Services Volume 2, Numbers 2&3, 2009.

[15] Salahuddin, M. A. et al. (2015). Introduction to wireless sensor networks. In Wireless sensor and mobile ad-hoc networks, 3-32. Springer, 2015.
https://doi.org/10.1007/978-1-4939-2468-4_1

[16] Spachos, P.; Hatzinakos, D. (2016). Real-time indoor carbon dioxide monitoring through cognitive wireless sensor networks. IEEE sensors journal, 16(2), 506-514, 2016.
https://doi.org/10.1109/JSEN.2015.2479647

[17] Ðurišic, M. P.; Tafa, Z.; Dimic, G.; Milutinovic, V. (2012). A survey of military applications of wireless sensor networks. In Embedded Computing (MECO), 2012 Mediterranean Conference on, pages 196-199. IEEE, 2012.

[18] Wang, Y.; Attebury, G.; Ramamurthy, B. (2006). A survey of security issues in wireless sensor networks, 2006.
https://doi.org/10.1109/COMST.2006.315852

[19] Webster, J. G.; Eren, H. (2017). Measurement, instrumentation, and sensors handbook: spatial, mechanical, thermal, and radiation measurement. CRC press, 2017.
https://doi.org/10.1201/b15664

[20] Yu, 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, 2012.
https://doi.org/10.1016/j.jnca.2011.03.005

[21] Zhang, Y.; He, S.; Chen, J. (2016). Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Transactions on Networking, 24(3), 1632-1646, 2016.
https://doi.org/10.1109/TNET.2015.2425146

[22] Zurawski, R. (2005). Embedded systems handbook. CRC press, 2005.
https://doi.org/10.1201/9781420038163
Published
2020-08-30
How to Cite
AHMAD, Ashraf et al. Reduce Effect of Dependent Malicious Sensor Nodes in WSNs using Pairs Counting and Fake Packets. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 5, aug. 2020. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3825>. Date accessed: 28 sep. 2020. doi: https://doi.org/10.15837/ijccc.2020.5.3825.

Keywords

Wireless Sensor Networks,Dependent Malicious Sensor Nodes,Detection and Prevention methods