隨著物聯網(IoT)的快速發展，物聯網裝置與人們的生活已經密不可分，因為物聯網裝置缺乏足夠的安全保護，使得紀錄在物聯網裝置中的個人隱私成為駭客攻擊的目標，除此之外，物聯網裝置能夠利用基礎設施連接(INF)以及裝置對裝置連接(D2D)來傳播訊息，INF基於社群網路為傳播目標，而D2D基於範圍受到移動性的影響，增大了物聯網的傳播能力，因此，要捕捉惡意程式在擁有兩種傳輸模式下的物聯網裝置動態傳播情形是一項很大的挑戰，我們提出Susceptible-Exposed-Infected-Recovered-Death (SEIRD)模型去模擬物聯網上惡意程式的傳播，SEIRD模型考慮了基於人類移動性的雙傳播，透過實驗證明了SEIRD模型良好捕捉惡意程式在不同狀況下的傳播情形，我們也提供了傳輸閥值作為判斷指標，及早知道當下的安全策略是否合適。

With the rapid development of the Internet of Things (IoT), IoT devices are insepa-rable from human’s life. Because the lack of security on the IoT devices, the personalprivacy we recorded on IoT devices has been targeted by the hackers. In addition tothat, IoT devices can utilize infrastructure (INF) link and device-to-device (D2D)link to propagate information. The INF link bases on the social network, and theD2D link bases on device’s mobility which amplifies the transmission capability.Those two propagation method problems with IoT device’s vulnerability make achallenge about capturing the dynamic malware propagation on IoT network. Weproposed Susceptible-Exposed-Infected-Recovered-Death (SEIRD) model to modelthe spread of IoT malware. SEIRD model considers two propagation method withhuman mobilty. We prove that our SEIRD model could well capture the spreadof IoT malware in different situations, and provide transmission threshold to learnearly whether the security measures are appropriate or not.

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