A Device-Based Secure Scheme Against PUEA Attacks in Cognitive Radio Sensor Networks

AUTHOR(S):

TITLE

A Device-Based Secure Scheme Against PUEA Attacks in Cognitive Radio Sensor Networks

ABSTRACT

Due to the resource-constrained sensor motes and critical security concerns, feasible wireless sensor-based systems require more breakthroughs in terms of network architecture, system design, and data processing techniques. In this paper, we incorporate the strengths of cognitive radio and the physical property of a device to improve the performance of a cognitive radio sensor network (CRSN) and resolve the security problem, considering one of the most destructive attacks in CRSNs called the primary user emulation attack (PUEA). Accordingly, we aim to develop a fully distributed method against PUEA attacks from two perspectives: (1) spectrum management with separate sensing and (2) device-based node identification, in order to explore the trade-off between spectrum management and the successful detection rate of malicious nodes. The proposed distributed secure algorithm with the knowledge of separate sensing allows the sensing sensors and the tasking nodes to perform a detection and identification mechanism such that dynamic spectrum management and correct spectrum decision can be achieved. The experimental results show that the proposed secure system provides a feasible way against the PUEA attacks.

KEYWORDS

primary user emulation attack, cognitive radio sensor networks, separate sensing, node identification

REFERENCES

[1] A. G. Fragkiadakis, E. Z. Tragos, and I. G. Askoxylakis, “A survey on security threats and detection techniques in cognitive radio networks,” IEEE Communications Surveys and Tutorials, vol. 15, pp. 428-445, 2013.

[2] R. Chen, J. Park, and J. H. Reed, “Defense against primary user emulation attacks in Cognitive Radio networks,” IEEE JSAC, vol. 26, no. 1, pp. 25-37, 2008.

[3] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 116-130, 2009.

[4] Z. Yuan, D. Niyato, H. Li, J. B. Song, and Z. Han, “Defeating primary user emulation attacks using belief propagation in cognitive radio networks,” IEEE J. Sel. Areas Commun., vol. 30, no. 10, pp. 1850-1860, Nov. 2012.

[5] B. Wild and K. Ramchandran, “Detecting Primary Receivers for Cognitive Radio Applications,” in Proc. of IEEE DySPAN, pp. 124-130, Nov. 2005.

[6] Z. Han and H. Jiang, “Replacement of Spectrum Sensing and Avoidance of Hidden Terminal for Cognitive Radio,” in Proc. of IEEE WCNC, pp. 1448-1452, Mar. 2008.

[7] C. Sun, W. Zhang, and K. B. Letaief, “Cooperative spectrum sensing for cognitive radios under bandwidth constraints,” in Proc. IEEE WCNC, Mar. 2007, pp. 1-5.

[8] S. Appadwedula, V. V. Veeravalli, and D. L. Jones, “Decentralized detection with censoring sensors,” IEEE Trans. Signal Process., vol. 56, no. 4, pp. 1362-1373, Apr. 2008.

[9] S. Maleki, A. Pandharipande, and G. Leus, “Energy-efficient distributed spectrum sensing for cognitive sensor networks,” IEEE Sensors J., vol. 11, no. 3, pp. 565-573, Mar. 2011.

[10] M. Najimi, et al., “A Novel Sensing Nodes and Decision Node Selection Method for Energy Ef- ficiency of Cooperative Spectrum Sensing in Cognitive Sensor Networks,” IEEE Sensors J., vol. 13, no. 5, pp. 1610-1621, May 2013.

[11] T. Chen, et al., “CogMesh: A Cluster-based Cognitive Radio Network,” 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 168-178, 2007.

[12] Yanchao Xu, et al., “A Cluster-based Energy Ef- ficient MAC Protocol for Multi-hop Cognitive Radio Sensor Networks,” IEEE GLOBECOM, 2012.

[13] Huazi Zhang, et al., “Distributed SpectrumAware Clustering in Cognitive Radio Sensor Networks,” IEEE GLOBECOM, 2011.

[14] Ozger, M. and Akan, O.B., “Event-driven Spectrum-Aware Clustering in Cognitive Radio Sensor Networks,” IEEE INFOCOM, 2013.

[15] R. Chen and J. M. Park, “Ensuring trustworthy spectrum sensing in cognitive radio networks,” in Proc. of IEEE Workshop on Networking Technol. for Software Defined Radio Networks (SDR) 2006, pp. 110-119, Sep. 2006.

[16] R. Chen, J. M. Park, and K. Bian, “Robust distributed spectrum sensing in cognitive radio networks,” in Proc. of IEEE Conference on Computer Communications (INFOCOM) 2008 miniconference, Apr. 2008.

[17] Z. Jin, S. Anand, and K. P. Subbalakshmi, “Detecting primary user emulation attacks in dynamic spectrum access networks,” in Proc. of the IEEE International Conference on Communications (ICC’09), June 2009.

[18] Z. Chen, T. Cooklev, C. Chen, and C. PomalazaRaez, “Modeling primary user emulation attacks and defenses in cognitive radio networks,” in Proc. of the 28th IPCCC, pp. 208-215, Dec. 2009.

[19] Y. Liu, P. Ning, and H. Dai, “Authenticating primary users’ signals in cognitive radio networks via integrated cryptographic and wireless link signatures,” in Proc. of the 31st IEEE Symposium on Security and Privacy (SP’10), pp. 286- 301, May 2010.

[20] C. N. Mathur and K. P. Subbalakshmi, “Digital signatures for centralized DSA networks,” in Proc. of the 4th Annual IEEE CCNC, pp. 1037- 1041, Jan. 2007.

[21] A. Alahmadi et al., “Defense Against Primary User Emulation Attacks in Cognitive Radio Networks Using Advanced Encryption Standard,” IEEE Trans. Inf. Forensic Secur., vol. 9, no. 5, pp. 772-781, May 2014.

[22] Wen, C. Y.; Sethares, W. A. “Automatic decentralized clustering for wireless sensor networks,” EURASIP JWCN 2005, 5, 686-697.

[23] Chih-Yu Wen and Ying-Chih Chen, “Dynamic Hierarchical Sleep Scheduling for Wireless AdHoc Sensor Networks,” Sensors, vol. 9, no. 5, pp. 3908-3941, May 2009.

[24] Shih-Chang Lin and Chih-Yu Wen, “DeviceBased Asynchronous Ranging and Node Identification for Wireless Sensor Networks,” IEEE Sensors Journal, vol. 14, no. 10, pp. 3648-3661, Oct. 2014.

Cite this paper

Shih-Chang Lin, Chih-Yu Wen. (2016) A Device-Based Secure Scheme Against PUEA Attacks in Cognitive Radio Sensor Networks. International Journal of Communications, 1, 117-126