Mobile Phone Firmware and Hardware Hacking Detection System
DOI:
https://doi.org/10.34190/iccws.20.1.3198Keywords:
Mobile phone hacking detection, mobile firmware analysis, mobile hardware analysis, machine learning, Android application analysisAbstract
Mobile devices have become prevalent due to the features they offer to their users, such as browsing the internet, digitising notes, sending and receiving invoices, asset management, recording signatures, checking emails and accessing social media platforms. In 2021, the number of mobile devices operating worldwide stood at almost 15 billion, expected to reach 18.22 billion by 2025. The sheer volume of sensitive information stored on these devices, from personal data to corporate credentials, makes them an enticing prospect for malicious actors. The increasing reliance on these mobile devices for personal and professional purposes underscores the importance of robust security measures. Modern hacking techniques often target mobile hardware and firmware vulnerabilities, jeopardising user privacy and data integrity. This research introduces the "Mobile Phone Firmware and Hardware Hacking Detection System", a comprehensive solution built with Python to detect unauthorised firmware and hardware modifications in mobile devices. The system integrates various modules, including tools for secure user interaction, machine learning-based for Android applications analysis, desktop user interface, and real-time threat detection. A meticulous review of existing research was conducted to gauge the current landscape of mobile phone hacking detection. The proposed system showcases innovative features like firmware attack detection, application behaviour analysis, and hardware integrity checks. This research addressed the escalating issue of mobile phone security by providing a system that can potentially thwart unauthorised access and data breaches. The system's implementation details include the user interface, Android app analysis, threat detection algorithm, firmware hack detector and the phone's low-level connector. Comparative analysis with existing solutions reveals the model's robustness in detecting hacking attempts while highlighting potential improvement areas. Although the system demonstrates significant capabilities, it is crucial to consider the potential challenges posed by more sophisticated firmware and hardware hacking techniques, such as those exploiting previously unknown vulnerabilities.
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Copyright (c) 2025 Michael Nhyk Ahimbisibwe, Noluntu Mpekoa

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.