arXiv subjects

Amirhossein Jamarani

Publications and source records attributed to Amirhossein Jamarani.

2 recordsLinked to original sources

arXiv2026

A Non-Line-of-Sight, Multi-Modality-based Side-Channel IP Theft Attack on Additive Manufacturing Using Dual Smartphones

Additive Manufacturing (AM) has revolutionized major sectors, including aerospace, automotive, and healthcare, by enabling adjustable production. As the usage of AM increases, so does the risk of Intellectual Property (IP) leakage during the printing process due to unintended side-channel emissions. Current studies and attack scenarios on 3D printers face three challenges: low success and accuracy rates in final G-code reconstruction, limited distance range for attacking the 3D printer's IP, and reliance on specialized, overt data-collection tools. This paper presents a side-channel attack that addresses the noted limitations by using two smartphones' internal sensors. We position the smartphones 60 cm away in a non-line-of-sight setup to collect the 3D printer's acoustic and magnetic emissions. Our attack successfully reconstructs the G-code commands of the final objects at a rate of 98.89% on command-level reconstruction accuracy. Additionally, we evaluate the transferability of our attack strategy by applying it to another 3D printer in a different environment. Our proven unauthorized access to the reconstructed G-code and thus to the IP of the AM system indicates the security weaknesses in 3D printing, highlighting the need for mitigating side-channel attacks.

cs.CR↗

arXiv2024

Practitioner Paper: Decoding Intellectual Property: Acoustic and Magnetic Side-channel Attack on a 3D Printer

The widespread accessibility and ease of use of additive manufacturing (AM), widely recognized as 3D printing, has put Intellectual Property (IP) at great risk of theft. As 3D printers emit acoustic and magnetic signals while printing, the signals can be captured and analyzed using a smartphone for the purpose of IP attack. This is an instance of physical-to-cyber exploitation, as there is no direct contact with the 3D printer. Although cyber vulnerabilities in 3D printers are becoming more apparent, the methods for protecting IPs are yet to be fully investigated. The threat scenarios in previous works have mainly rested on advanced recording devices for data collection and entailed placing the device very close to the 3D printer. However, our work demonstrates the feasibility of reconstructing G-codes by performing side-channel attacks on a 3D printer using a smartphone from greater distances. By training models using Gradient Boosted Decision Trees, our prediction results for each axial movement, stepper, nozzle, and rotor speed achieve high accuracy, with a mean of 98.80%, without any intrusiveness. We effectively deploy the model in a real-world examination, achieving a Mean Tendency Error (MTE) of 4.47% on a plain G-code design.

cs.CR↗