论文标题
标称模型的隐形测量杆型攻击
Stealthy Measurement-Aided Pole-Dynamics Attacks with Nominal Models
论文作者
论文摘要
当通过名义模型实施传统的杆动力攻击（TPDA）时，精确模型和名义模型之间的模型不匹配通常会影响其隐身性，甚至会使隐形失去。为了解决这个问题，我们当前的论文提出了一种新颖的隐形测量杆型攻击（MAPDAS）方法，方法是模型不匹配。首先，揭示了使用精确模型的TPDA的局限性，确切的模型有助于确保TPDA的隐身性，但模型不匹配严重影响其隐身性。其次，为了处理模型不匹配，提出的MAPDA方法是通过使用模型参考自适应控制策略来设计的，该策略可以保持隐身性。此外，与需要测量和控制输入的现有方法相比，仅需要测量，就更容易实现。第三，使用多元测量的收敛性探索了所提出的MAPDA方法的性能，而具有模型不匹配的MAPDA具有与TPDA相同的隐形和相似的破坏性。具体而言，具有自适应收益的MAPDA将在可接受的检测阈值下保持隐秘，直到发生破坏性。最后，来自网络倒置的摆系统的实验结果证实了该方法的可行性和有效性。
When traditional pole-dynamics attacks (TPDAs) are implemented with nominal models, model mismatch between exact and nominal models often affects their stealthiness, or even makes the stealthiness lost. To solve this problem, our current paper presents a novel stealthy measurement-aided pole-dynamics attacks (MAPDAs) method with model mismatch. Firstly, the limitations of TPDAs using exact models are revealed, where exact models help ensure the stealthiness of TPDAs but model mismatch severely influences its stealthiness. Secondly, to handle model mismatch, the proposed MAPDAs method is designed by using a model reference adaptive control strategy, which can keep the stealthiness. Moreover, it is easier to implement as only the measurements are needed in comparison with the existing methods requiring both the measurements and control inputs. Thirdly, the performance of the proposed MAPDAs method is explored using convergence of multivariate measurements, and MAPDAs with model mismatch have the same stealthiness and similar destructiveness as TPDAs. Specifically, MAPDAs with adaptive gains will remain stealthy at an acceptable detection threshold till destructiveness occurs. Finally, experimental results from a networked inverted pendulum system confirm the feasibility and effectiveness of the proposed method.
