Security and Privacy in Communication Networks. 7th International ICST Conference, SecureComm 2011, London, UK, September 7-9, 2011, Revised Selected Papers

Research Article

Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-31909-9_9,
        author={Chowdhury Hyder and Brendan Grebur and Li Xiao},
        title={Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks},
        proceedings={Security and Privacy in Communication Networks. 7th International ICST Conference, SecureComm 2011, London, UK, September 7-9, 2011, Revised Selected Papers},
        proceedings_a={SECURECOMM},
        year={2012},
        month={10},
        keywords={Cognitive Radio Network SSDF attack 802.22},
        doi={10.1007/978-3-642-31909-9_9}
    }
    
  • Chowdhury Hyder
    Brendan Grebur
    Li Xiao
    Year: 2012
    Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks
    SECURECOMM
    Springer
    DOI: 10.1007/978-3-642-31909-9_9
Chowdhury Hyder1,*, Brendan Grebur1,*, Li Xiao1,*
  • 1: Michigan State University
*Contact email: hydercho@cse.msu.edu, greburbr@cse.msu.edu, lxiao@cse.msu.edu

Abstract

IEEE 802.22 is the first standard based on the concept of cognitive radio. It recommends collaborative spectrum sensing to avoid the unreliability of individual spectrum sensing while detecting primary user signals. However, it opens an opportunity for attackers to exploit the decision making process by sending false reports. In this paper, we address security issues regarding distributed node sensing in the 802.22 standard and discuss how attackers can modify or manipulate their sensing result independently or collaboratively. This problem is commonly known as spectrum sensing data falsification (SSDF) attack or Byzantine attack. To counter the different attacking strategies, we propose a reputation based clustering algorithm that does not require prior knowledge of attacker distribution or complete identification of malicious users. We compare the performance of our algorithm against existing approaches across a wide range of attacking scenarios. Our proposed algorithm displays a significantly reduced error rate in decision making compared to current methods. It also identifies a large portion of the attacking nodes and greatly minimizes the false detection rate of honest nodes.