DOI QR코드

DOI QR Code

Rule-Based Anomaly Detection Technique Using Roaming Honeypots for Wireless Sensor Networks

  • Received : 2015.09.04
  • Accepted : 2016.08.02
  • Published : 2016.12.01

Abstract

Because the nodes in a wireless sensor network (WSN) are mobile and the network is highly dynamic, monitoring every node at all times is impractical. As a result, an intruder can attack the network easily, thus impairing the system. Hence, detecting anomalies in the network is very essential for handling efficient and safe communication. To overcome these issues, in this paper, we propose a rule-based anomaly detection technique using roaming honeypots. Initially, the honeypots are deployed in such a way that all nodes in the network are covered by at least one honeypot. Honeypots check every new connection by letting the centralized administrator collect the information regarding the new connection by slowing down the communication with the new node. Certain predefined rules are applied on the new node to make a decision regarding the anomality of the node. When the timer value of each honeypot expires, other sensor nodes are appointed as honeypots. Owing to this honeypot rotation, the intruder will not be able to track a honeypot to impair the network. Simulation results show that this technique can efficiently handle the anomaly detection in a WSN.

Keywords

References

  1. S.H. Jokhio, I.A. Jokhio, and A.H. Kemp, "Node Capture Attack Detection and Defence in Wireless Sensor Networks," IET Wireless Sensor Syst., vol. 2, no. 3, Sept. 2012, pp. 161-169. https://doi.org/10.1049/iet-wss.2011.0064
  2. M. Conti, R.D. Pietro, and A. Spognardi, "Clonewars: Distributed Detection of Clone Attacks in Mobile WSNs," J. Comput. Syst. Sci., vol. 80, no. 3, May 2014, pp. 654-669. https://doi.org/10.1016/j.jcss.2013.06.017
  3. H. Shafiei et al., "Detection and Mitigation of Dink Hole Sttacks in Wireless Sensor Networks," J. Comput, Syst. Sci., vol. 80, no. 3, May 2014, pp. 644-653. https://doi.org/10.1016/j.jcss.2013.06.016
  4. A. Abduvaliyev et al., "On the Vital Areas of Intrusion Detection Systems in Wireless Sensor Networks," IEEE Commun. Surveys Tuts., vol. 15, no. 3, 2013, pp. 1223-1237. https://doi.org/10.1109/SURV.2012.121912.00006
  5. S. Shamsh and V. Dubey, "Roaming Honeypots along with IDS in Mobile Ad-Hoc Networks," Int. J. Comput. Appl., vol. 69, no. 23, May 2013.
  6. M.A. Rassam, A. Zaina, and M.A. Maarof, "Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: a Survey and Open Issues," Sensors, vol. 13, no. 8, 2013, pp. 10087-10122. https://doi.org/10.3390/s130810087
  7. G. Han et al., "IDSEP: a Novel Intrusion Detection Scheme Based on Energy Prediction in Cluster-based Wireless Sensor Networks," IET Inform. Security, vol. 7, no. 2, June 2013, pp. 97-105. https://doi.org/10.1049/iet-ifs.2012.0052
  8. S. Shamshirband et al., "D-FICCA: A Density-based Fuzzy Imperialist Competitive Clustering Algorithm for Intrusion Detection in Wireless Sensor Networks," Meas., vol, 55, Sept. 2014, pp. 212-226. https://doi.org/10.1016/j.measurement.2014.04.034
  9. P. Abduvaliyev et al., "On the Vital Areas of Intrusion Detection Systems in Wireless Sensor Networks," IEEE Commun. Surveys Tuts., vol. 15, no. 3, 2013, pp. 1223-1237. https://doi.org/10.1109/SURV.2012.121912.00006
  10. S.S. Bhojannawar, C.M. Bulla, and V.M. Danawade, "Anomaly Detection Techniques for Wireless Sensor Networks - a Survey," Int. J. Adv. Res. Comput. Commun. Eng., vol. 2, no. 10, Oct. 2013.
  11. A. Canovas et al., "Web Spider Defense Technique in Wireless Sensor Networks," Int. J. Distrib. Sensor Netw., vol. 10, no. 7, July 2014, pp. 1-7.
  12. E. Karapistoli and A.A. Economides, "ADLU: a Novel Anomaly Detection and Location-Attribution Algorithm for UWB Wireless Sensor Networks," EURASIP J. Inform. Security, vol. 2014, no. 3, 2014.
  13. F. Bao et al., "Hierarchical Trust Management for Wireless Sensor Networks and its Applications to Trust-Based Routing and Intrusion Detection," IEEE Trans. Netw. Service Manag., vol. 9, no. 2, June 2012, pp. 169-183. https://doi.org/10.1109/TCOMM.2012.031912.110179
  14. B. Sun et al., "Anomaly Detection Based Secure In-network Aggregation for Wireless Sensor Networks," IEEE Syst. J., vol. 7, no. 1, Mar. 2013, pp. 13-25. https://doi.org/10.1109/JSYST.2012.2223531
  15. M. Xie, J. Hu, and S. Guo, "Segment-Based Anomaly Detection with Approximated Sample Covariance Matrix in Wireless Sensor Networks," IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 2, Feb. 2013, pp. 574-583. https://doi.org/10.1109/TPDS.2014.2308198
  16. Network Simulator. http:///www.isi.edu/nsnam/ns

Cited by

  1. Sensors Anomaly Detection of Industrial Internet of Things Based on Isolated Forest Algorithm and Data Compression vol.2021, pp.None, 2021, https://doi.org/10.1155/2021/6699313