{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:03:25Z","timestamp":1785402205243,"version":"3.56.0"},"reference-count":23,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T00:00:00Z","timestamp":1780790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["\u2116388\/PTF-24-26"],"award-info":[{"award-number":["\u2116388\/PTF-24-26"]}]},{"award":["\u2116388\/PTF-24-26"],"award-info":[{"award-number":["\u2116388\/PTF-24-26"]}],"id":[{"id":"https:\/\/ror.org\/05tyne317","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical damage to smartphones creates a persistent bottleneck in mobile forensic practice: once a device can no longer be accessed through its operating system, conventional logical acquisition fails, and investigators face a choice between accepting data loss and escalating to hardware-level intervention. This paper describes an integrated forensic workflow that addresses this gap by combining In-System Programming (ISP) and Chip-Off memory extraction with an AI-assisted artifact localization and prioritization layer. The workflow was evaluated on 18 physically damaged Android smartphones for which all standard acquisition paths were unavailable. Hardware extraction produced verified binary memory images from all 18 devices. A 1D-CNN localization classifier subsequently screened those images, achieving F1-score = 0.88 and ROC-AUC = 0.94 on the synthetic test partition. Prioritization of candidate windows reduced manual review volume by 78%, cut total expert review time by 63%, and shortened the time to first relevant artifact from 42 to 14 min relative to unassisted examination (indicative estimates based on three examiner sessions; no inferential statistical test was performed). The study contributes a formalized, criteria-driven decision model for selecting between ISP and Chip-Off, which are experimentally validated thermal extraction profiles for eMMC, UFS, and PoP\/RAM memory.<\/jats:p>","DOI":"10.3390\/s26123639","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T05:43:46Z","timestamp":1780897426000},"page":"3639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Assisted ISP and Chip-Off Forensic Framework for Damaged Android Devices"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3382-4685","authenticated-orcid":false,"given":"Leila","family":"Rzayeva","sequence":"first","affiliation":[{"name":"Research and Innovation Center \u201cCyberTech\u201d, Astana IT University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aigerim","family":"Alibek","sequence":"additional","affiliation":[{"name":"Research and Innovation Center \u201cCyberTech\u201d, Astana IT University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Altynbay","family":"Abdykassym","sequence":"additional","affiliation":[{"name":"Digital Heritage of Eurasia LLP, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"Zhakenov","sequence":"additional","affiliation":[{"name":"Research and Innovation Center \u201cCyberTech\u201d, Astana IT University, Astana 010000, Kazakhstan"},{"name":"Digital Heritage of Eurasia LLP, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1016\/j.procs.2024.04.126","article-title":"A Systematic Literature Review on Digital Forensic Investigation on Android Devices","volume":"235","author":"Almuqren","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cuomo, R., D\u2019Agostino, D., and Ianulardo, M. (2022). Mobile Forensics: Repeatable and Non-Repeatable Technical Assessments. Sensors, 22.","DOI":"10.3390\/s22187096"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ayers, R., Brothers, S., and Jansen, W. (2014). Guidelines on Mobile Device Forensics, National Institute of Standards and Technology. NIST Special Publication 800-101 Revision 1.","DOI":"10.6028\/NIST.SP.800-101r1"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"da Silveira, C.M., de Sousa, R.T., de Oliveira Albuquerque, R., Nze, G.D.A., de Oliveira J\u00fanior, G.A., Orozco, A.L.S., and Garc\u00eda Villalba, L.J. (2020). Methodology for Forensics Data Reconstruction on Mobile Devices with Android Operating System Applying In-System Programming and Combination Firmware. Appl. Sci., 10.","DOI":"10.3390\/app10124231"},{"key":"ref_5","first-page":"7","article-title":"Chip-off Success Rate Analysis Comparing Temperature and Chip Type","volume":"9","author":"Ence","year":"2019","journal-title":"J. Digit. Forensics Secur. Law"},{"key":"ref_6","first-page":"116","article-title":"Secure Chip-Off Method with Acoustic-based Fault Diagnostics for IoT and Smart Grid Data Recovery","volume":"9","author":"Yermekov","year":"2025","journal-title":"Int. J. Smart Grid"},{"key":"ref_7","first-page":"301675","article-title":"A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response","volume":"48","author":"Dunsin","year":"2024","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_8","first-page":"301737","article-title":"Artificial Intelligence in Mobile Forensics: A Survey of Current Status, a Use Case Analysis and AI Alignment Objectives","volume":"49","author":"Vasilaras","year":"2024","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_9","first-page":"301617","article-title":"Digital Forensics and Strong AI: A Structured Literature Review","volume":"46","author":"Honekamp","year":"2023","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_10","first-page":"301844","article-title":"DFPulse: The 2024 Digital Forensic Practitioner Survey","volume":"51","author":"Hargreaves","year":"2024","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_11","first-page":"301973","article-title":"AI-Driven Dataset Creation in Mobile Forensics Using LLM-Based Storyboards","volume":"55","author":"Pawlaszczyk","year":"2025","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_12","first-page":"302030","article-title":"Practitioner-Driven Framework for AI Adoption in Digital Forensics","volume":"55","author":"Veksler","year":"2025","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_13","first-page":"301439","article-title":"Revisiting the Dataset Gap Problem-On Availability, Assessment and Perspective of Mobile Forensic Corpora","volume":"43","author":"Dolos","year":"2022","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_14","first-page":"301587","article-title":"Assessing Data Remnants in Modern Smartphones after Factory Reset","volume":"46","author":"Blankesteijn","year":"2023","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhetpisbayeva, A., Shayea, I., Kozhakhmet, Z., Alibek, A., Nyssanov, N., and Baibussinov, A. (2025). The Effect of TRIM Function on Data Recovery from SSD Solid-State Drives. Proceedings of the 2025 IEEE\/ACIS 29th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing (SNPD 2025-Summer), Busan, Republic of Korea, 25\u201327 June 2025, IEEE.","DOI":"10.1109\/SNPD65828.2025.11254695"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kim, H., Shin, Y., and Shon, T. (2022). Digital Forensic Analysis to Improve User Privacy on Android. Sensors, 22.","DOI":"10.3390\/s22113971"},{"key":"ref_17","unstructured":"(2012). Guidelines for Identification, Collection, Acquisition and Preservation of Digital Evidence (Standard No. ISO\/IEC 27037:2012)."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Do, Q., Martini, B., and Choo, K.K.R. (2015). A Forensically Sound Adversary Model for Mobile Devices. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0138449"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Karlsson, K.J., and Glisson, W.B. (2014). Android Anti-forensics: Modifying CyanogenMod, 2014. Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS 2014), Waikoloa, HI, USA, 6\u20139 January 2014, IEEE Computer Society.","DOI":"10.1109\/HICSS.2014.593"},{"key":"ref_20","first-page":"84","article-title":"Multi-platform Process Flow Models and Algorithms for Extraction and Documentation of Digital Forensic Evidence from Mobile Devices","volume":"16","author":"Bongomin","year":"2022","journal-title":"East Afr. J. Inf. Technol."},{"key":"ref_21","first-page":"301169","article-title":"A New Model for Forensic Data Extraction from Encrypted Mobile Devices","volume":"38","author":"Fukami","year":"2021","journal-title":"Forensic Sci. Int. Digit. Investig."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Daraghmi, E., Qaroush, Z., Hamdi, M., and Cheikhrouhou, O. (2023). Forensic Operations for Recognizing SQLite Content (FORC): An Automated Forensic Tool for Efficient SQLite Evidence Extraction on Android Devices. Appl. Sci., 13.","DOI":"10.3390\/app131910736"},{"key":"ref_23","unstructured":"Barrios, R.M., and Lehrfeld, M.R. (2011, January 25\u201327). Ios Mobile Device Forensics: Initial Analysis. Proceedings of the Annual ADFSL Conference on Digital Forensics, Security and Law, Richmond, VA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T05:52:03Z","timestamp":1780897923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,7]]},"references-count":23,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123639"],"URL":"https:\/\/doi.org\/10.3390\/s26123639","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,7]]}}}