Novel Mobile Forensics Method for Non-Standard Low-End Chipset-Based Feature Phones Examination

The increasing misuse of low-cost feature phones, particularly those built on undocumented and non-standard chipset architectures, poses significant challenges for digital forensic practitioners. Conventional forensic suites such as UFED, XRY, Oxygen Forensics, and MOBILedit demonstrate high reliability for smartphones; however, their effectiveness drops sharply when dealing with non-branded devices lacking standardized firmware and bootloader documentation. Existing guidelines, including NIST SP 800-101 and SWGDE best practices, also do not provide structured procedures for chipset detection or the acquisition of unsupported devices, resulting in frequent evidence loss during cybercrime investigations. This study introduces a novel, systematic mobile forensics framework that integrates PID/VID-based chipset identification, clone SIM boot validation, and heuristic key combination mapping to enable memory dump extraction from undocumented feature phones. A deterministic algorithm is developed to automate the detection-to-extraction pipeline, ensuring forensic soundness and repeatability. The proposed method was experimentally validated on 115 low-end feature phones, achieving 92.17% chipset detection accuracy, 88.69% bootloader activation success, and 87% complete memory dump recovery, significantly outperforming standard procedures referenced in NIST guidelines. Critical artifacts, including SMS (92%), call logs (85%), and contacts (81%), were successfully recovered, with deleted data fragments retrieved in 43% of cases. These findings demonstrate that the proposed method substantially extends forensic capabilities beyond the scope of existing commercial tools and provides law enforcement agencies with a reliable, legally defensible process for extracting digital evidence from non-standard devices. Future directions include the integration of AI-driven chipset recognition, automated key-mapping, and cloud-based forensic libraries to further strengthen scalability and global applicability.