Author ORCID Identifier


Defense Date


Document Type


Degree Name

Doctor of Philosophy


Computer Science

First Advisor

Irfan Ahmed


Additive Manufacturing (AM) refers to a group of manufacturing processes that create physical objects by sequentially depositing thin layers. AM enables highly customized production with minimal material wastage, rapid and inexpensive prototyping, and the production of complex assemblies as single parts in smaller production facilities. These features make AM an essential component of Industry 4.0 or Smart Manufacturing. It is now used to print functional components for aircraft, rocket engines, automobiles, medical implants, and more. However, the increased popularity of AM also raises concerns about cybersecurity. Researchers have demonstrated strength degradation attacks on printed objects by injecting cavities in the design file which cause premature failure and catastrophic consequences such as failure of the attacked propeller of a drone during flight.

Since a 3D printer is a cyber-physical system that connects the cyber and physical domains in a single process chain, it has a different set of vulnerabilities and security requirements compared to a conventional IT setup. My Ph.D. research focuses on the cybersecurity of one of the most popular AM processes, Material Extrusion or Fused Filament Fabrication (FFF). Although previous research has investigated attacks on printed objects by altering the design, these attacks often leave a larger footprint and are easier to detect. To address this limitation, I have focused on attacks at the intermediate stage of slicing through minimal manipulations at the individual sub-process level. By doing so, I have demonstrated that it is possible to implant subtle defects in printed parts that can evade detection schemes and bypass many quality assessment checks.

In addition to exploring attacks through design files or network layer manipulations, I have also proposed firmware attacks that cause damage to the printed parts, the printer, and the printing facility.

To detect sabotage attacks on FFF process, I have developed an attack detection framework that analyzes the cyber and physical domain state of the printing process and detects anomalies using a series of estimation and comparison algorithms in time, space, and frequency domains. An implementation case study confirms that cyber-physical security frameworks are an effective solution against sophisticated sabotage attacks. The increasing use of 3D printing technology to produce functional components underscores the growing importance of compliance and regulations in ensuring their quality and safety. Currently, there are no standards or best practices to guide a user in making a critical printing setup forensically ready. Therefore, I am proposing a novel forensic readiness framework for material extrusion-based 3D printing that will guide standards organizations in formulating compliance criteria for important 3D printing setups. I am optimistic that my offensive and defensive research endeavors presented in this thesis will serve as a valuable resource for researchers and industry practitioners in creating a safer and more secure future for additive manufacturing.


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