The Sleep Deprivation Attack in Sensor Networks: Analysis and Methods of Defense

Abstract

The ability of sensor nodes to enter a low power sleep mode is very useful for extending network longevity. We show how adversary nodes can exploit clustering algorithms to ensure their selection as cluster heads for the purpose of launching attacks that prevent victim nodes from sleeping. We present two such attacks: the barrage attack and the sleep deprivation attack. The barrage attack bombards victim nodes with legitimate requests, whereas the sleep deprivation attack makes requests of victim nodes only as often as in necessary to keep the victims awake. We show that while the barrage attack causes its victims to spend slightly more energy, it is more easily detected and requires more effort on behalf of the attacker. Thus, we have focused our research on the sleep deprivation attack. Our analysis indicates that this attack can nullify any energy savings obtained by allowing sensor nodes to enter sleep mode. We also analyze three separate methods for mitigating this attack: the random vote scheme, the round robin scheme, and the hash-based scheme. We have evaluated these schemes based upon their ability to reduce the adversary's attack, the amount of time required to select a cluster head, and the amount of energy required to perform each scheme. We have found that of the three clustering methods analyzed, the hash-based scheme is the best at mitigating the sleep deprivation attack.

Keywords:

• Energy Attacks
• Secure Cluster Head Election
• Distributed Algorithms
• Sensor Networks

Acknowledgments

This research is sponsored by the Defense Advance Research Projects Agency (DARPA), and administered by the Army Research Office under Emergent Surveillance Plexus MURI Award No. DAAD19-01-1-0504. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies. This work is also supported by NSF CAREER 0093085 and DARPA/MARCO GSRC Grant. Sencun Zhu's work is supported by NSF Grant CNS-0524156.

Notes

6. S. Capkun, and J. Hubaux, Secure Positioning in Sensor Networks. Tech. rep., EPFL/IC/200444, 2004.

8. D. W. Carman, P. S. Kruus, and B. J. Matt, Constraints and Approaches for Distributed Sensor Network Security. Tech. rep., NAI Labs #00–010, 2000

22. J. Krishnaswami, Denial-of-Service Attacks on Battery-Powered Mobile Computers, Master's thesis, Virginia Polytechnic Institute and State University, 2003

23. P. Kyasanur, and N. Vaidya, Detection and Handling of Mac Layer Misbehavior in Wireless Networks. DSN: Dependable Systems and Networks, San Francisco, CA, June 22–25 2003

31. J. Shin, L. J. Guibas, and F. Zhao, A Distributed Algorithm for Managing Multi-Target Identities in Wireless Ad-Hoc Sensor Networks, Information Processing In Sensor Networks, pp. 223–238, 2003.

32. F. Stajano, Security for Ubiquitous Computing, John Wiley & Sons, Ltd., New York, NY, June 6–9, 2004, Hyatt Harborside, Boston, MA, USA. 2002.