本論文探討的主題是感知無線電(cognitive radio, CR)如何在惡意的環境中辨識出攻擊者的存在。感知無線電被認為是解決當前頻譜使用效率不佳的重要技術，但隨著此技術的成熟發展，進而衍伸出許多值得探討的安全問題，其中較為人所研究的攻擊為模仿主要使用者攻擊(primary user emulation attacks, PUEA)，這裡提出使用無線通道的特性對模仿主要使用者攻擊做辨識。

在無線環境下，不同接收端與傳送端之間的通道統計特性都是獨一無二的，因此可以將此特性當成是一種簽證指紋( ngerprint)，利用感知無線電的頻譜偵測能力，便能透過無線通道的獨特性辨識出主要使用者與模仿主要使用者之身份，藉由此方法進而透過底層直接進行認證而提高偵測效率。這種在實體層的認證(physical-layer authentication)機制，相較於一般傳統網路協定，即資訊必需要透過不同層級的通報到達上層後才能對傳訊端做身分辨識(identi cation)，實體層的認證更能縮短認證的時間，並藉由感知無線電的智慧型學習功能，進一步達成跨層級的認證(cross-layer authentication)目的。

經由上述所提出的實體層的認證機制雖然可以快速分辨訊號發射端身分，但單一節點的偵測可能會受到許多因素而影響偵測結果，這裡提出序列概率比試驗(sequential probability ratio test, SPRT)的合作式偵測，並且與常見的合作式偵測，奈曼-皮爾生(Neyman-Pearson)固定樣本大小試驗( fixed sample size test, FSST)方法做比較，可以發現在給定相同的誤警率(probability of false alarm)下，要 達到相同的偵測機率(probability of detection)，SPRT所需要的樣本數會比FSST還要 少，這意味著SPRT可以用更快的偵測速度來達到與FSST相同的效能，提升合作式感知無線電中實體層認證的效率。

The topic of this paper is to identify the attacker's presence in cognitive radio (CR), which is considered as an important technology to solve the current ine cient use of
spectrum. Recently, with the maturity of the development of CR, many security issues are discovered. The most important one is primary user emulation attack (PUEA). In this work, we propose a method by using the characteristics of wireless channels to identify the PUEA.

In the wireless environment, the statistical property of the wireless channel between the receiver and transmitter is unique, therefore, we can use this feature as a ngerprint. By employing the capability of spectrum sensing in CR, we can identify the primary user from primary user emulation attacker via the uniqueness of wireless channel. By this method, we can directly authenticate on the physical layer and improve the detection e ciency. Compared with conventional authentication scheme based on the higher layer protocols, whose information must be passed to the upper layers, the proposed scheme using physical layer is more e cient in terms of the detection time. For the whole authentication scheme, we also devise an intelligent learning capability to achieve the cross-layer authentication.

Although one can quickly identify the transmitter by the proposed physical layer
authentication mechanism, but the detection result of a single node may be in
uenced
by many factors. Hence, we propose the cooperative detection by using the sequential
probability ratio test (SPRT), and compare it with the Neyman-Pearson xed sample
size test (FSST) method. We nd that, to achieve the same probability of detection
and under the given false alarm rate, the required sample number of SPRT is less than
that of FSST, which means that SPRT can achieve the same performance as the FSST,
while being faster than FSST. Via the SPRT, the detection e ciency of physical layer authentication in CR can be further enhanced.

[1] Federal Communications Commission, "Spectrum policy task force report." ET Docket No.02-135, 2002.
[2] 林高洲,感知無線電之運用與發展",中華民國電子零件認證委員會," IECQ報導第五十二期,2008.
[3] J. Mitola III, "Cognitive radio: An integrated agent architecture for software defi ned radio."PhD thesis, Royal Institute of Technology (KTH), Stockholm, Sweden, May 2000.
[4] Alexander M. Wyglinski, Maziar Nekovee, and Y. Thomas Hou, "Cognitive Radio Communications and Networks," Academic Press, 2009.
[5] S. Haykin, "Cognitive radio: Brain-empowered wireless communications," IEEE Journal on Selected Areas in Communications, vol. 23, pp. 201-220, Feb.2005.
[6] W. Wang, "Denial of service attacks in cognitive radio networks," 2010 International Conference on Environmental Science and Information Application Technology (ESIAT) , vol. 2, pp. 530-533, Jul. 2010.
[7] J. L. Burbank, "Security in cognitive radio networks: the required evolution in approaches to wireless network security," 3rd International Conference on Cognitive
Radio Oriented Wireless Networks and Communications, 2008. (Crown COM 2008.) , pp. 1-7, May 2008.
[8] T. C. Clancy, N. Goergen, "Security in Cognitive Radio Networks: Threats and Mitigation," 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2008. (Crown COM 2008.), pp. 1-8, May 2008.
[9] R. Chen, J. M. Park, J. H. Reed, "Defense against Primary User Emulation Attacks in Cognitive Radio Networks," IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 25-37, Jan. 2008.
[10] Federal Communication Commission, "Notice for Proposed Rulemaking (NPRM 03-322): Facilitating Opportunities for Flexible E cient, and Reliable Spectrum Use Employing Cognitive Radio Technologies," ET Docket, No.03-108, Dec.2003.
[11] Z. Chen, T. Cooklev, C. Chen, P. R. Carlos, "Modeling primary user emulation attacks and defenses in cognitive radio networks," Performance Computing and Communications Conference (IPCCC), 2009 IEEE 28th International , pp. 208-
215, Dec. 2009.
[12] C. Zhao, L. Xie, X. Jiang, L. Huang, Y. Yao, "A PHY-layer authentication approach for transmitter identi cation in cognitive radio networks," 2010 International Conference on Communications and Mobile Computing (CMC) , vol.2, pp.
154-158, Apr. 2010.
[13] L. Huang, L. Xie, H. Yu, W. Wang, Y. Yao, "Anti-PUE Attack Based on Joint Position Veri cation in Cognitive Radio Networks," 2010 International Conference on Communications and Mobile Computing (CMC), vol.2, pp. 169-173, Apr. 2010.
[14] S. Chaudhari, V. Koivunen, H.V. Poor, "Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios," IEEE Transactions on Signal Processing , vol.57, no.7, pp. 2690-2700, Jul. 2009.
[15] Carlos Cordeiro et al., "IEEE 802.22：The rst worldwide standard based on Cognitive Radios," 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 328-337, Baltimore, MD, USA,8-11 Nov. 2005.
[16] J. K. Tugnait and H. Kim, "A channel-based hypothesis testing approach to enhance user authentication in wireless networks," Proc: IEEE COMSNETS, pp. 1-9, 2010.
[17] T. W. Anderson, "Introduction to Multivariate Statistical Analysis," New York: John Wiley and Sons, 1958.
[18] M. P. Olivieri, G. Barnett, A. Lackpour, A. Davis, P. Ngo, "A scalable dynamic spectrum allocation system with interference mitigation for teams of spectrally agile software de ned radios," 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005.(DySPAN 2005.), pp. 170-179, Nov. 2005.
[19] K. Challapali, S. Mangold, and Z. Zhong, "Spectrum agile radio: Detecting spectrum opportunities," in Proceedings of the Sixth Annual International Symposium
on Advanced Radio Technologies, Mar. 2004.
[20] "Guidelines for evaluation of radio transmission technologies for IMT-2000," Recommendation ITU-R M.1225, International Telecommunication Union, 1997.
[21] A. Wald, "Sequential Test of Statistical Hypothesis," Annals Math. Statistics, vol. 16. No. 2, pp. 117-186, Jun. 1945.