This paper addresses the estimation and control of linear systems in the presence of adversarial attacks on sensors or actuators. The authors first characterize the resilience of a system to attacks and propose an efficient algorithm to estimate the state despite the attacks, inspired by error-correction over the reals and compressed sensing. They then show that increasing the resilience of the system can be achieved by changing the parameters, specifically by implementing a state-feedback law. In the second part, the authors focus on designing output-feedback controllers that stabilize the system despite attacks, demonstrating that the estimation and stabilization problems are equivalent. The paper provides theoretical results and numerical simulations to support the proposed methods.This paper addresses the estimation and control of linear systems in the presence of adversarial attacks on sensors or actuators. The authors first characterize the resilience of a system to attacks and propose an efficient algorithm to estimate the state despite the attacks, inspired by error-correction over the reals and compressed sensing. They then show that increasing the resilience of the system can be achieved by changing the parameters, specifically by implementing a state-feedback law. In the second part, the authors focus on designing output-feedback controllers that stabilize the system despite attacks, demonstrating that the estimation and stabilization problems are equivalent. The paper provides theoretical results and numerical simulations to support the proposed methods.