Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of system identification of linear time invariant systems when some of the sensor measurements are changed by a malicious adversary.  treat adversaries as omniscient and impose no restrictions (statistical or otherwise) on how they can alter the measurements of the sensors under attack. Given a bound on the number of attacked sensors, and under a certain observability condition,  show that  can construct models that are useful for certain control purposes, e.g., stabilization.  also provide a precise characterization of the equivalence relation that identifies which models cannot be distinguished in the presence of attacks.
['Mehrdad Showkatbakhsh', 'Paulo Tabuada', 'Suhas N. Diggavi']
I am writing a research paper on a idea System identification in the presence of adversarial outputs
[{"from": "human", "value": "I am writing a research paper on a idea System identification in the presence of adversarial outputs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of system identification of linear time invariant systems when some of the sensor measurements are changed by a malicious adversary.  treat adversaries as omniscient and impose no restrictions (statistical or otherwise) on how they can alter the measurements of the sensors under attack. Given a bound on the number of attacked sensors, and under a certain observability condition,  show that  can construct models that are useful for certain control purposes, e.g., stabilization.  also provide a precise characterization of the equivalence relation that identifies which models cannot be distinguished in the presence of attacks."}]