DEVICESHIELD

Author

Krishvanth K R, Rajkumar S, Nikhil Dev P J ,Mrs. N. G. Dharaniya


Abstract

 Device owners rarely hold a record of the original hardware configuration of their laptops, so after servicing it is difficult to check whether components such as RAM, storage, GPU or motherboard have been replaced or changed, and manual checking is slow and unreliable. This paper presents DeviceShield AI, a web-based hardware integrity monitoring and comparison system for Windows computers. The system scans the processor, motherboard, BIOS, memory modules, storage drives, graphics adapter, battery and network adapters through Windows Management Instrumentation, stores each scan as a hardware profile, and compares a later profile with an earlier one. A rule-based module checks the identity of each component, counts the differences and assigns a LOW, MEDIUM or HIGH risk level with recommendations; it is not a trained machine-learning model. The back end is written in Python with Flask and keeps user accounts, devices, hardware profiles and comparison reports in a SQLite database, and a browser dashboard presents the results. In the hosted mode each user has a private account, and a small loopback agent on the user’s own computer performs the scan. The system was run on a Lenovo laptop with an AMD Ryzen 5 7235HS processor, where the saved scan history reached scan number 115 and a comparison of two consecutive scans reported no component changes. Ten constructed scenarios and twenty-one API checks were executed. The scenarios confirmed the behaviour of the rules and the API checks confirmed per-account privacy, while the tests also exposed limitations such as a failure for computers without a battery and false alarms from virtual network adapters. The reported differences require manual verification and do not, by themselves, prove unauthorized hardware replacement.


Keywords

Hardware Integrity, Hardware Inventory, Windows Management Instrumentation, Baseline Comparison, Rule-Based Risk Classification, Servicing Verification, Flask, SQLite.



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References


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