A New Approach to Detect Stealthy on . During the experiment, researchers used 2B as a target device with 1GB memory and a 900MHz quad-core ARM Cortex A7 processor, with a combination of a PA 303 BNC and . This setup was able to detect three and their families with an accuracy of 99.82% and 99.61%.

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@TeddyTheBest I'm friends with a guy who pioneered a similar technique on 3D printers. They monitored the electrical usage of the printer to categorize different particular motor movements with an eye towards identifying malware/hijacked prints. It's super cool to see a similar idea elsewhere :3

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