Shelby Hall Graduate Research Forum Posters

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Description

Additive Manufacturing (AM) is a newer famlily of production tecchologies that constructs objects by fusing layers of material into the desired shape. Methods for achieving this as described in Gibson et al. [1] are varied and include Fused Filament Deposition, Selective Laser Sintering, Stereolithography (SLA), and Powder Bed Fusion. Computers are integral to the processes being responsible for creating and decoding design instructions, collecting and processing sensor data, and, ultimately, directing the activity of the machines which implement the process. Additionally, the AM industry is rapidly expanding, worth an estimated $23 billion in 2023 and projected to reach $88 billion by 2030 [2]. The coupling of these factors makes AM systems lucritive targets for cyberattacks and criminal activity.

To be ready to address the threat of cyberattacks and criminal activity, law enforcement and organizations develop forensic processes that allow them to determine what happened to systems during a compromise. In opposition to these efforts, criminals attempt to eliminate, alter, or even fabricate evidence to hamper, misdirect, or compromise the investigation. Harris [3] notes that while efforts to mask or destroy the primary evidence of the malicous activity are generally effective, the tools used to obscure the primary evidence may leave evidence of its use behind. Much of the literature in this space has relies on observations from non-academic sources or proof of concept demonstrations of tools used to perform anti-forensic activities. This work will empirically examine the effectiveness of operating system tools for anti-forensic activity and residual data generated by the use of those tools.

Publication Date

3-2026

Department

Computer Science

Disciplines

Cybersecurity | Data Science | Law

Evaluating the Effects of Anti-Forensic Activities in Additive Manufacturing Devices

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