Voice over Internet Protocol (VoIP) applications (apps) provide convenient and low cost means for users to communicate and share information with each other in real-time. Day by day, the popularity of such apps is increasing, and people produce and share a huge amount of data, including their personal and sensitive information. This might lead to several privacy issues, such as revealing user contacts, private messages or personal photos. Therefore, having an up-to-date forensic understanding of these apps is necessary. This chapter presents analysis of forensically valuable remnants of three popular Mobile VoIP (mVoIP) apps on Google Play store, namely: Viber, Skype, and WhatsApp Messenger, in order to figure out to what extent these apps reveal forensically valuable information about the users activities. We performed a thorough investigative study of these three mVoIP apps on smartphone devices. Our experimental results show that several artefacts, such as messages, contact details, phone numbers, images, and video files, are recoverable from the smartphone device that is equipped with these mVoIP apps.
In order to facilitate the forensic investigation of mobile devices that are rapidly changing in their structure, Do et al. @cite_0 proposed a forensically sound adversary model. Azfar et al. @cite_21 considered this adversary model as a template to map a potential adversary's capabilities and constraints, in order to evaluate the usefulness of such a model by carrying out a forensic analysis of five popular Android social apps (Twitter, POF Dating, Snapchat, Fling and Pinterest). They showed that useful artefacts are recoverable using this model, including databases, user account information, contact lists, images and profile pictures. They could also discover timestamps for notifications and tweets, as well as a Facebook authentication token string used by the apps.
@cite_0 @cite_21
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In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device. Android forensics is one of the most studied topics in the mobile forensics literature, partly due to the popularity of Android devices and apps. However, there does not appear to have a formal model that captures the activities undertaken during a forensic investigation. In this paper, we adapt a widely used adversary model from the cryptographic literature to formally capture a forensic investigator's capabilities during the collection and analysis of evidentiary materials from mobile devices. We demonstrate the utility of the model using five popular Android social apps (Twitter, POF Dating, Snapchat, Fling and Pinterest). We recover various information of forensic interest, such as databases, user account information, sent-received images, profile pictures, contact lists, unviewed text messages. We are also able to determine when a notification was sent, a tweet was posted, as well as identifying the Facebook authentication token string used in the apps.