Digital Forensic Analysis of Hybrid Scooter Motors using Smart Flow and Integrated Digital Forensics Standard

Yerly Ania Saputri, Ahmad Fazal, Aditya Wahyu Ningrat, Dedy Hariyadi

Submitted : 2025-07-21, Published : 2025-11-30.

Abstract

This research addresses the challenges of digital forensics for connected hybrid vehicles, focusing on the Yamaha Fazzio hybrid scooter. The study highlights how limited collection methods and mobile device encryption often compromise the integrity of electronic evidence. To address these issues, a five-stage framework was developed, combining guidelines from NIST SP 800-101 Rev.1 and ISO/IEC 27037:2012. This comprehensive framework includes data collection, evidence identification, forensic acquisition, examination and analysis, and final reporting. The framework's effectiveness is boosted by Smart Flow automation on Cellebrite UFED devices, which automates the identification of Android devices, extraction of Y-Connect App databases, GPS logs in JSON, and travel route thumbnails. This automation significantly enhances the efficiency of the acquisition and analysis processes while maintaining evidence integrity. Evaluations showed successful data acquisition from a Xiaomi Mi 5s Plus with the Y-Connect App. Details from the riding_log table were extracted, providing information on travel routes, distance, average and maximum speeds, and estimated fuel consumption during a Yogyakarta - Klaten travel test scenario. These results are crucial for developing digital forensics SOPs for other connected hybrid vehicles in future research.

Keywords

Digital Forensics; Hybrid Scooter Motor; Xiaomi; Cellebrite UFED; Yamaha Fazio

References

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http://dx.doi.org/10.28989/compiler.v14i2.3211

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