A NOVEL ACOUSTIC-ASSISTED CHIP-OFF FRAMEWORK FOR DATA EXTRACTION FROM DAMAGED HARD DISK DRIVES
DOI:
https://doi.org/10.37943/25IIRG2444Keywords:
signal classification, machine learning, forensic analysis, SMART attributes, logical malfunction, environmental sound recognition, chip-off, data recoveryAbstract
This article discusses best practices for extracting data from damaged mobile phones and hard drives while maintaining the integrity of the storage hardware. It emphasizes that data recovery is essential for digital forensics and cybersecurity due to a common approach to data recovery from mobile devices. In many cases, step-by-step low-level collections instead of quick logical groups reveal hidden artifacts or recently deleted files. Sometimes, this is the only reliable option.
Hard disk errors are usually divided into two categories: logical errors and physical damage. The recovery platform combines proven diagnostics, predictive analysis, and specially designed tools, ranging from installing a magnetic head and replacing an image disk to changing file system settings to make the data readable again. One of the new ideas is acoustic perception of the environment. Just as a device listens to the sound of a running engine, it listens to an acoustic response that can be used to automate the detection of mechanical defects. Tics or stuttering can tell you a lot. The study includes two models: one for detecting problems on the hard drive and the other for data recovery. Model A detects errors related to noise, and Model B tries to recover the data. Thus, this study uses a combined approach to extract data from a damaged hard drive.
With the proliferation of devices for the Internet of Things, acoustic-enabled chip disconnection methods provide forensic protection for repairing and inspecting damaged equipment, such as sensors and damaged industrial components. These results should be of interest to research groups, corporate lawyers, and criminologists in terms of broader coverage and reliability of data recovery operations.
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