# Digital Forensics Readiness in Big Data Networks:
## 3 Literature Review
### Background  
In the rapidly evolving landscape of information technology, the advent of big data has revolutionised the way organisations manage and analyse vast volumes of data [2; 4; 6; 14]. According to [PERSON] [6], big data analytics denotes the use of advanced techniques on large datasets, combining the vast scale of big data with sophisticated analytics to drive a major trend in BI. Big data technologies [4], particularly within wireless networks, present both opportunities and challenges that make wireless networks inherently more susceptible to attacks compared to their wired counterparts, due to factors such as signal interception and unauthorised access [15; 16; 17]. As organisations increasingly rely on big data for critical operations, ensuring the security and integrity of these networks becomes paramount. Security incidents and data privacy issues within big data ecosystems have garnered significant attention in recent years [10]. The sheer volume, variety, and velocity [6] of data in big data environments make them attractive targets for cybercriminals seeking to exploit vulnerabilities and compromise sensitive information. Furthermore, the prioritisation of BI needs and profits over personal data privacy by some big tech organisations remains a cause for concern [9]. Examples of big tech organisations that have faced criticism in the past for prioritising BI needs and profits over personal data privacy include Facebook, Google, Amazon, Apple, and Microsoft [9]. Incidents such as unethical practises, data breaches, unauthorised access, and insider threats pose significant risks to organisations, highlighting the importance of implementing robust security measures and proactive monitoring mechanisms [8; 10]  
DFR plays a crucial role in mitigating the impact of security incidents and ensuring auditable accountability within organisations [18] as well as big data environments. DFR is derived from the term digital forensics. According to [PERSON] [18] (p. 11), digital forensics is denoted as scientific methodologies, principles, and techniques applied to law to ensure the admissibility of digital evidence in a court of law. The researcher also discussed [PERSON]'s exchange principle [18] (pp. 11-12). The principle states that anything or anyone interacting with an environment (including digital environments) takes something or leaves behind something (evidence). A forensically ready digital environment (DFR) would thus be well suited to capture this evidence to aid in future investigations. DFR involves setting up policies, procedures, and technical systems that allow for the swift and accurate collection, preservation, and analysis of digital evidence [16; 18]. By proactively preparing for potential security breaches and establishing forensic capabilities, organisations can effectively investigate and respond to incidents, thereby minimising damage and preserving evidential data for legal proceedings. Aside from security breaches, [PERSON] [18] (pp. 65-66) discussed other \"multiple business scenarios where digital forensics can be applied to manage business risk\". These include cybercrime impact validation, solving disputes (internal and external), compliance adherence, electronic discovery (e-discovery) support, and support for contractual and commercial agreements. However, conducting digital forensics investigations within complex big data ecosystems, particularly those built on Apache Hadoop, still presentsunique challenges as discussed by the researchers [PERSON] et al. [2], [PERSON] et al. [11], [PERSON] [19], and [PERSON] et al. [20]. Apache Hadoop's distributed file system (HDFS) has emerged as the most preferred platform for processing and analysing big data [2; 21] due to its scalability, fault tolerance, and cost-effectiveness. Its ecosystem of tools and frameworks, including Apache Spark, offers unparalleled capabilities for distributed computing and data processing.