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An approach to detection of modification attacks on digital 3D models in smart additive manufacturing

The paper presents a study of the issues of detecting attacks on modification of digital models of products (details) intended for 3D printing in modern intelligent additive manufacturing systems. In general, such systems are networks that include multiple 3D printers (i. e. 3D farms) operating in parallel, capable of printing series of products at user requests, for instance elements of physical structures of robots and vehicles, blades of unmanned aerial vehicles and other parts made of plastic, metal and other materials. Existing examples of such 3D installations are vulnerable to the actions of attackers who try to make a hidden unauthorized modification by influencing digital models. After such an attack, end products may have a design defect with visual characteristics that are almost indistinguishable from the original sample of such a product. For instance, by influencing a defective element of the UAV body, an attacker may reduce its controllability and even lead to its crash. The paper considers an experimental substantiation of the hypothesis on the possibility of detecting modification attacks on digital models of products based on processing and analysis of the program code of such models. The features of defects in 3D product models presented in the G-code language and selected from open 3D model databases are analyzed. A data set consisting of original and modified product models is compiled. An approach to modification detection using embedding to transform data into numerical vectors and train classifiers on them using supervised learning methods is proposed. Experiments on test data samples demonstrated the feasibility of the proposed approach to modification detection and the prospects for its further development and application in practice. Read more...