由於具有敏捷性和機動性，無人機已被廣泛應用於民用和軍事任務。為了遠程控制與監視無人機的飛行動態，位置和軌跡資訊等任務相關數據需要通過無線通道傳輸。然而，無線通道的廣播性與空中通訊環境的廣泛覆蓋範圍，使無人機網路容易受到竊聽攻擊。在本論文中，我們研究了被動式攻擊者於無人機網路中的潛在安全威脅，這些攻擊者目標為竊聽並利用機器學習技術來解碼已加密的位置資訊。我們透過模擬表明，使用簡易的神經網路模型能夠解碼現有位置保護方法所加密的位置資訊。為了抵禦此基於機器學習的攻擊，我們建議一種基於隨機線性網路編碼並結合隨機置換加密密鑰的位置隱私保護方法。我們證明了所提出的方法能提供不可追蹤性並降低攻擊者的攻擊成功率。模擬結果表明，即使群集中僅有少數的無人機，我們的方法在攻擊者的位元錯誤率方面也優於現有的位置保護方法。;Thanks to the agility and mobility features, unmanned aerial vehicles (UAVs) have been applied for a wide range of civil and military missions. To remotely control and monitor UAVs, mission-related data such as location and trajectory information are transmitted over wireless channels. However, UAV networks are vulnerable to eavesdropping attacks due to: 1) the broadcasting nature of wireless channels; 2) the broad coverage in aerial environments. In this paper, we investigate the potential security threats in UAV networks with passive attackers who aim to eavesdrop and decode encrypted locations by using machine learning techniques. We show that a neural network of two hidden layers is able to decode the encrypted locations if using the existing location protection methods. To defend against such machine learning based attacks, we suggest a location protection approach based on the random linear network coding with encryption keys being randomly permuted. We prove that our proposed approach allows for a low attacker’s success probability and provides untraceability property. Our simulation results indicate that our approach significantly outperforms the existing location protection methods in terms of attacker’s bit error rate, even with a small number of UAVs.
