![]() ![]() ![]() We focus on three digital forensic areas, namely memory, disk space, and network forensics. Whatsapp messenger theverge android#More precisely, we present the results of the forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications. In this contribution, we present a detailed forensic analysis of Cisco WebEx which is among the top three videoconferencing applications available today. ![]() Whatsapp messenger theverge how to#Furthermore, this paper shows how to determine the properties of both the broadcast and the group communications in which the user has been involved, as well as how to reconstruct the logs of the voice and video calls.ĭigital forensic analysis of videoconferencing applications has received considerable attention recently, owing to the wider adoption and diffusion of such applications following the recent COVID-19 pandemic. In the results obtained, it is shown how to reconstruct the list of contacts, the history of exchanged textual and non-textual messages, as well as the details of their contents. Then, the methods of analyzing the artefacts are revealed, aiming to understand how they can be correlated to cover all the possible evidence. Since the data is stored in an encrypted SQLite database, its decryption is first discussed. ![]() Once generated, their storage location and database structure on the device were identified. In order to provide a complete interpretation of the artefacts, a set of controlled experiments to generate these artefacts were performed. In this paper, a forensic analysis of the artefacts left by the encrypted WhatsApp SQLite databases on unrooted Android devices is presented. Originally designed for simple and fast communication, however, its privacy features, such as end-to-end encryption, eased private and unobserved communication for criminals aiming to commit illegal acts. WhatsApp is the most popular instant messaging mobile application all over the world. We then show that the application is effective when applied to a set of real world cases, demonstrating a performance increase in terms of accuracy that could exceed 30\(\%\) when compared to traditional approaches. We adopt a machine translation approach by providing an algorithm that takes messages of a smartphone as input, and processes them to a target language in an innovative way. The problems that make this analysis difficult are three: (1) the content could be written in a language that is not spoken by the analyst, (2) the number of messages actually containing pertinent and relevant traits is a small percentage on a potentially quite large space and (3) texts could be rather noisy in terms of content, for they could contain emoticons, language loans, and slang terms (beyond the fact that they could also be written in obscure languages such as specific dialects or languages spoken by small communities). This analysis has the specific objective of determining which messages (either text or vocal), transmitted from and received by a specific device, seized for forensic analysis, may contain data that are relevant in a criminal investigation. In this paper we introduce an innovative application of translation techniques applied to the problem of forensics analysis of smartphones. ![]()
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