## 2 Related Work  
Numerous studies have contributed to the ever-evolving field of digital forensics [7; 11; 13; 19; 20; 21; 22; 23; 24], shedding light on various aspects of data retrieval and investigative techniques. This section highlights some key findings from recent studies. This article [4] proposes a novel approach to mobile forensic acquisition. Forensic investigators encounter a significant hurdle due to the ever-changing landscape of mobile devices and their software. Manufacters frequently introduce new models with varying security features and operating systems, necessitating constant adaptation of forensic methods. Moreover, the growing adoption of encryption by consumers presents an additional challenge, rendering it increasingly complex for forensic experts accessing device data. Hence, it remains crucial for forensic investigators to remain current with the latest developments in mobile technology and innovate data-extraction techniques capable of keeping abreast of these advancements. In this research [5], the authors have delineated and implemented a procedure rooted in an agile methodology for crafting forensic investigation modules. Furthermore, we have elucidated the implementation specifics of modules encompassing nine widely used Android social-media and instant-messaging applications, encompassing aspects like wireless communication and system information. Ultimately, we have encapsulated the challenges confronted during the course of this study. This research [6] introduces a technique for enhancing Log2 Timeline to comprehensively extract temporal data from iOS devices. To achieve this, the authors develop a parser plugin tailored for Log2 Timeline specifically designed to address any missing artifacts, such as \"plists\" or SQLite databases. The method is outlined as follows: Initially, we generate a forensic timeline utilizing the Plaso tool, using a previously acquired iOS image. Subsequent to this, we scrutinize the timeline for any gaps or missing artifacts. Following this, we devise a Plaso plugin engineered to parse these absent artifacts. Finally, we re-run Plaso, incorporating the new plugins, to construct a more exhaustive timeline. This process ultimately yields a thorough forensic timeline derived from the forensic image of an iOS device. Experimental results demonstrate that the integration of additional plugins significantly enhances the comprehensiveness of the forensic timeline extracted from iOS devices. This paper [25] undertakes a comprehensive review of the existing literature to gain insights into the previously accomplished body of work. Moreover, we leverage this existing literature as a foundation to introduce a unified digital business forensic investigation (DBFI) process, employing design science research methodology. This unified DBFI process is structured around three essential categories: planning, preparation, and pre-response; acquisition and preservation; and analysis and reconstruction. Additionally, our DBFI framework has been meticulously designed to eliminate any potential confusion or ambiguity while offering practitioners a systematic approach to conducting DBFI with a heightened level of assurance. This paper [8] delves into the heightened encryption and security safeguards present in contemporary mobile devices, highlighting their influence on conventional forensic data extraction approaches used in law enforcement. We illustrate how tackling encryption hurdles necessitates the adoption of novel mobile forensic methodologies centered around circumventing security features and capitalizing on system vulnerabilities. We introduce a fresh forensic acquisition model underpinned by a legal framework that emphasizes the usability of digital evidence acquired through vulnerability exploitation. In a study focused on Android smart phones, the authors in [9] conducted the forensic analysis on different social networking apps: Whisper, Wickr, Instagram, WeChat, and LINE.