I’ve Got You, Under My Skin: Biohacking Augmentation Implant Forensics
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
Recently, people have become interested in embedding technology in their bodies to augment themselves with new abilities. For example, a person may embed a chip in their hand to wirelessly lock and unlock a door. Subdermal augmentation implants, the implant technology that can add these new abilities to a user, are increasing in popularity. With this new technology comes a variety of new forensics and security challenges. In our work, we conceive a modified forensics approach for augmentation implants, which includes device discovery and its associated forensic acquisition and memory analysis. First, we explore three device discovery methods: implant chip reading, X-Ray detection and the use of metal detectors. We then share a case study by implementing an augmentation implant authentication system, acquiring and analyzing its memory. Our results show that when an implant is installed in raw chicken meat, that X-Ray scanners are capable of not only unveiling it, but revealing the exact type of implant to a trained analyst. In the case of metal detectors, only one of the implants were detected, and our results indicate deeply installed implants (1.5 cm or more below the skin) are undetectable. In the case of using RFID and NFC scanners to read compatible chips, we found we could detect the implants up to 1.6 cm and 1.0 cm respectively. We also examined the potential legal and ethical issues surrounding augmentation implant forensics, highlighting cases in which surgical removal could potentially be legally mandated.
Publication Source (Journal or Book title)
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
First Page
315
Last Page
332
Recommended Citation
Seiden, S., Baggili, I., & Ali-Gombe, A. (2024). I’ve Got You, Under My Skin: Biohacking Augmentation Implant Forensics. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 571 LNICST, 315-332. https://doi.org/10.1007/978-3-031-56583-0_21