Secure Chip-Off Method with Acoustic-based Fault Diagnostics for IoT and Smart Grid Data Recovery

Rzayeva L. Imanberdi A. Alibek A. Myrzatay A. Yermekov Y. Kayisli K. Feldman G.
September 2025 ilhami Colak

International Journal of Smart Grid
2025 #9 Issue 3 116 - 126 pp.

This article explores modern methods for extracting information from faulty mobile devices, hard disk drives (HDDs), and solid-state drives (SSDs) while considering the physical integrity of data storage components. In the digital era, recovering data from damaged devices is crucial for forensic investigations, corporate security, and information protection. The study examines existing data extraction techniques for mobile devices, including both software-based and hardware-based approaches such as JTAG, SPI, UFI Box, and the “Chip-off” method. It highlights the importance of low-level data access; as logical extraction methods often fail to recover deleted or hidden files. For HDDs, the paper classifies possible failures into logical and physical damage categories. It discusses data recovery mechanisms, ranging from diagnosing disk health and analyzing SMART attributes to utilizing specialized recovery tools and hardware techniques, such as replacing the magnetic head assembly (MHA) and reconstructing the file system. Additionally, the work incorporates an Environmental Sound Recognition (ESR) module to enable the automated detection of mechanical failures based on acoustic signatures. As the adoption of IoT devices with onboard storage accelerates, ensuring secure, reliable, and forensic-ready data recovery methods becomes increasingly important. The proposed chip-off method with acoustic diagnostics supports critical security and privacy needs in IoT ecosystems by enabling recovery and analysis of compromised or tampered edge devices. The research contributes to the advancement of forensic analysis and data recovery techniques, offering valuable insights for law enforcement agencies, private investigators, and cybersecurity professionals. This methodology not only enhances forensic capabilities but also supports data recovery within secure smart grid environments and IoT-based infrastructures, where device tampering and data breaches are critical concerns.

acoustic diagnostics , AI Diagnostics , environmental sound recognition (ESR) , IoT Data Integrity , magnetic head assembly (HSA) , physical damage , Smart Grid Security

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Research and Innovation Center “CyberTech”, Astana IT University, Astana, 010000, Kazakhstan
Dean’s Office, Astana IT University, Astana, 010000, Kazakhstan
Department of Information security, Faculty of Information Technologies, L.N. Gumilyov, Eurasian National University, Astana, 010000, Kazakhstan
Gazi University, Eng. Fac. Electrical-Electronic Eng., Ankara, Turkey
Birmingham City University, Faculty of Computing, College of Computing, United Kingdom

Research and Innovation Center “CyberTech”
Dean’s Office
Department of Information security
Gazi University
Birmingham City University

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