ABSTRACT
Web applications have experienced a widespread adaptation owing to the agile Service Oriented Architecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication records, email addresses, profile pictures, history, downloads, bookmarks, cache, cookies, etc. We explore how different Random Access Memory (RAM) sizes of client devices impact the persistence and format of extracted memory artifacts. A memory artifact extraction tool is developed to automate the extraction of artifacts identified via unstructured string analysis. Google Meet forensic artifacts are critical in that they are potential digital evidence in relevant criminal investigations. Additionally, they highlight that user data can be extracted despite implementing multiple privacy and security mechanisms. © 2022 The Author(s)
ABSTRACT
Web applications have experienced a widespread adaptation owing to the agile Service Oriented Ar-chitecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication records, email addresses, profile pictures, history, downloads, bookmarks, cache, cookies, etc. We explore how different Random Access Memory (RAM) sizes of client devices impact the persistence and format of extracted memory artifacts. A memory artifact extraction tool is developed to automate the extraction of artifacts identified via unstructured string analysis. Google Meet forensic artifacts are critical in that they are potential digital evidence in relevant criminal investigations. Additionally, they highlight that user data can be extracted despite implementing multiple privacy and security mechanisms.(c) 2022 The Author(s). Published by Elsevier Ltd on behalf of DFRWS This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ABSTRACT
A service (social) robot is defined as the Internet of Things (IoT) consisting of a physical robot body that connects to one or more Cloud services to facilitate human-machine interaction activities to enhance the functionality of a traditional robot. Many studies found that anthropomorphic designs in robots resulted in greater user engagement. Humanoid service robots usually behave like natural social interaction partners for human users, with emotional features such as speech, gestures, and eye-gaze, referring to the users’ cultural and social background. During the COVID-19 pandemic, service robots play a much more critical role in helping to safeguard people in many countries nowadays. This paper gives an overview of the research issues from technical and social-technical perspectives, especially in Human-Robot Interaction (HRI), emotional expression, and cybersecurity issues, with a case study of gamification and service robots. © 2022, Springer Nature Switzerland AG.