In this project, we investigate an online detection mechanism for malicious attempts on additive manufacturing (AM) systems, which taps into optical images, accelerometer data, and thermal video signals collected during the printing process. Novel cyberattacks are designed and implemented on part g-code files. A new real-time AM process authentication according to artificial intelligence will be developed to leverage in-situ data for real-time alteration detection during AM prints.