感知無線電 (Cognitive Radio; CR) 被設計用來提昇頻譜的使用效率。未授權的次要使用者 (Secondary User; SU) 利用CR的能力感測附近的環境並且利用尚未被授權的首要使用者 (Primary User; PU) 使用的空頻譜來達成SU之間的傳輸，並且要在傳輸過程當中盡量不干擾PU。但是SU很可能因為具有一些自私、貪婪的天性或者因為錯誤的資訊而違反既有的規則與PU同時進行傳輸以獲取額外的利益。透過這些缺點我們提出了一個分散式阻斷服務攻擊，惡意使用者 (Malicious User; MU) 將會假扮成故障的SU，並藉由使用者貪婪的天性引誘正常的SU與PU同時進行傳輸，進而攻擊整個無線網路。引誘SU直接進行干擾，MU在這種攻擊中很難被偵測出來。

我們採用隨機幾何來模型化PU、SU和MU，並分析這三者之間互相干擾的情形。我們將用演化賽局理論來分析對於每一個使用不同存取策略的SU，他們將得到不同的利益並且造成不同的干擾。在破窗理論中我們可以得知使用者會有自私、貪婪的天性，也就是這些使用者會因為觀察到其他人正在做壞事而也想跟著一起違反規則。透過模擬結果我們可以觀察在不同的利益參數配置下，正常的SU以及受到引誘不正常的SU之數量變化。其結果也顯示在引誘式攻擊中，只需要一小部份的MU就可以造成大範圍的攻擊。我們也設計了簡單的防禦策略來偵測出MU，藉此研究促進感知無線電網路的實做及發展。

Cognitive radio technology, which is designed to enhance spectrum utilization, depends on the success of opportunistic access, where secondary users (SUs) exploit spectrum void unoccupied by primary users (PUs) for opportunistic transmissions. However, SUs of a selfish and greedy nature or of misleading information may not stick to the liability rule and will make concurrent transmissions with PUs for additional incentives. By exploiting this vulnerability, this paper proposes a novel distributed denial-of-service (DoS) attack where randomly distributed malicious users (MUs) posing as malfunctioning SUs cooperatively induce originally behaved SUs to execute concurrent transmissions with PUs and thus collapse the cognitive radio network. Hiding behind SUs who directly inject interference, the malicious attack initiators are hard to identify and to detect.

We adopt stochastic geometry to model the spatial distributions of PUs, SUs, and MUs to analyze the mutual interference among them. The access strategy of each SU, which evolves with the experienced payoffs and interference, is modeled by an evolutionary game. Through the broken window theory, we knew that users have the greedy and selfish nature. That is, when user observed that the others are misbehaved, they will also attempt to break the rule. We conduct extensive simulation experiments to investigate the population of behaved and misbehaved SUs under the different setups on payoffs. The results show that our attack requires smaller number of cooperative MUs to achieve a wider range of damage. We also design a simple detection model to identity the existence of MU, which facilitate the development and deployment of cognitive radio ad hoc networks.

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