Deception Against Data-Driven Linear-Quadratic Control
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911456064176128 |
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| author | Fotiadis, Filippos Kanellopoulos, Aris Vamvoudakis, Kyriakos G. Topcu, Ufuk |
| author_facet | Fotiadis, Filippos Kanellopoulos, Aris Vamvoudakis, Kyriakos G. Topcu, Ufuk |
| contents | Deception is a common defense mechanism against adversaries with an information disadvantage. It can force such adversaries to select suboptimal policies for a defender's benefit. We consider a setting where an adversary tries to learn the optimal linear-quadratic attack against a system, the dynamics of which it does not know. On the other end, a defender who knows its dynamics exploits its information advantage and injects a deceptive input into the system to mislead the adversary. The defender's aim is to then strategically design this deceptive input: it should force the adversary to learn, as closely as possible, a pre-selected attack that is different from the optimal one. We show that this deception design problem boils down to the solution of a coupled algebraic Riccati and a Lyapunov equation which, however, are challenging to tackle analytically. Nevertheless, we use a block successive over-relaxation algorithm to extract their solution numerically and prove the algorithm's convergence under certain conditions. We perform simulations on a benchmark aircraft, where we showcase how the proposed algorithm can mislead adversaries into learning attacks that are less performance-degrading. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deception Against Data-Driven Linear-Quadratic Control Fotiadis, Filippos Kanellopoulos, Aris Vamvoudakis, Kyriakos G. Topcu, Ufuk Systems and Control Deception is a common defense mechanism against adversaries with an information disadvantage. It can force such adversaries to select suboptimal policies for a defender's benefit. We consider a setting where an adversary tries to learn the optimal linear-quadratic attack against a system, the dynamics of which it does not know. On the other end, a defender who knows its dynamics exploits its information advantage and injects a deceptive input into the system to mislead the adversary. The defender's aim is to then strategically design this deceptive input: it should force the adversary to learn, as closely as possible, a pre-selected attack that is different from the optimal one. We show that this deception design problem boils down to the solution of a coupled algebraic Riccati and a Lyapunov equation which, however, are challenging to tackle analytically. Nevertheless, we use a block successive over-relaxation algorithm to extract their solution numerically and prove the algorithm's convergence under certain conditions. We perform simulations on a benchmark aircraft, where we showcase how the proposed algorithm can mislead adversaries into learning attacks that are less performance-degrading. |
| title | Deception Against Data-Driven Linear-Quadratic Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2506.11373 |