Ghost in the Machine: Securing Industrial Control Systems with Adaptive Watermarks
Imagine a competitor subtly tweaking the code of your precision milling machine, causing it to produce slightly flawed parts. These flaws are imperceptible to the naked eye, but eventually lead to catastrophic failures. How can you prove your designs were stolen and manipulated?
The key lies in a dynamic watermarking technique, powered by reinforcement learning. Instead of embedding a static digital signature, we inject a tiny, imperceptible "ghost" into the machine's control signals. This "ghost" (the watermark) subtly alters the machine's operation, leaving a unique, verifiable fingerprint without compromising performance. The ingenuity is in how this watermark adapts in real-time.
Think of it like subtly adjusting the steering of a self-driving car to create a unique driving style, undetectable to the passenger, but identifiable through analysis of the steering commands. Our system dynamically adjusts the watermark's strength and shape based on the current operating conditions and feedback from a detection algorithm. The system uses reinforcement learning to optimize the watermark, balancing detectability, energy consumption and performance.
Here's a simplified pseudocode snippet demonstrating the concept:
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