The use of Android devices has increased rapidly in recent years, increasing the chance of hacking and crime. Hackers target smartphones for various purposes, including getting sensitive information, financial fraud, identity theft, and other crimes. As a result, Android users must be aware of these possible dangers and take necessary measures to secure their smartphones. Because smartphones are the primary repository of personal sensitive information, smartphone designers must include security measures and encourage users to install freely available security software. Most studies have evaluated facial recognition as the most secure feature. This paper shows the uses of a facial recognition application to protect user files that contain sensitive information. The application uses machine-learning algorithms, specifically a Convolutional Neural Network (CNN) for face recognition that detects the user's face, tries to access the file, compares it with the basic image in the local file, and gives the result of whether to open the file or reject depending on the compared image. The application addresses critical concerns and improves file privacy features on Android devices, ensuring user file safety, and achieving success with 99% accuracy. It can also distinguish the faces of women wearing a shawl and people wearing glasses.

The Internet of Things (IoT) driven Industrial Revolution 4.0 (IR4.0) and this is impacting every sector of the global economy. With IoT devices, everything is computerized. Today's digital forensics is no longer limited to computers, mobiles, or networks. The current digital forensics landscape demands a significantly different approach. The traditional digital forensics frameworks no longer meet the current requirements. Therefore, in this paper, we propose a novel framework called “Multi-level Artifact of Interest Digital Forensics Framework for IoT” (MAoIDFF-IoT). The keynote "Multi-level" aims to cover all levels of the IoT architecture. Our novel IoT digital forensics framework focuses on the Artifact of Interest (AoI). Additionally, it proposes the action/detection matrix. It encompasses the advantages of the previous frameworks while introducing new features specifically designed to make the framework suitable for current and future IoT investigation scenarios. The MAoIDFF-IoT framework is designed to face the challenges of IoT forensic analysis and address the diverse architecture of IoT environments. Our proposed framework was evaluated through real scenario experiments. The evaluation of the experimental results reveals the superiority of our framework over existing frameworks in terms of usability, inclusivity, focus on the (AoI), and acceleration of the investigation process.