# Investigation of Smart Machines with DNAs in SpiderNet
## 2 Related Works
This dispersion and fragmentation of data reduce accessibility, as evidence may be stored on multiple servers located in different countries or regions, each subject to distinct jurisdictional rules [20]. These jurisdictional differences can restrict access or necessitate legal clearances for each location, making it challenging to retrieve complete datasets and establish an unbroken chain of custody [21]. The situation is further complicated by regulations such as the European Union's General Data Protection Regulation (GDPR), which imposes strict limitations on foreign investigators attempting to access data stored in European data centres [22]. Additionally, multi-tenancy in cloud environments increases the risk of data contamination, as multiple users share the same infrastructure resources [23]. These complexities underscore the need for novel forensic methodologies and collaborative models between investigators and CSPs. Such approaches must address the inherent limitations of cloud systems, streamline legal processes, and enhance the efficiency of forensic investigations while ensuring compliance with data protection and jurisdictional regulations. Several cloud forensic methodologies and frameworks have been proposed to address the challenges associated with the cloud domain. One such approach is ForensiBlock [24], which leverages a provenance-driven blockchain framework for secure data ownership and access using Role-Based Access Control (RBAC-SA) and Merkle-root verification. However, this framework imposes a high computational overhead and suffers from limited interoperability with a non-blockchain system. The NIST Cloud Computing Forensic Reference Architecture (CC FRA) [25] is another prominent framework, addressing data accessibility and ownership by identifying forensic challenges and proposing mitigation strategies for cloud systems. The multi-layered CC FRA integrates the Cloud Security Alliance's (CSA) Enterprise Architecture and NIST's Cloud Security Reference Architecture to enhance forensic readiness. While this framework offers a robust foundation for forensic preparation, its extensive design requires substantial customisation to fit organisational requirements, potentially reducing its portability across different cloud providers or jurisdictions. Additionally, its intricate mapping of over 347 functional capabilities and 62 forensic concerns could strain resources, particularly for smaller organisations. The Cloud Forensic Preparedness Framework (CFRF) [26] provides a structured approach to improving cloud forensic readiness. By addressing data ownership and accessibility through legal, technological, and organisational preparedness, the CFRF lays a solid foundation for forensic investigations. However, its complexity may pose challenges for smaller organisations due to the high costs associated with legal compliance, technology acquisition, and workforce training. Moreover, the framework's extensive adaptation requirements for diverse organisational needs or cloud service providers may hinder its scalability and interoperability in multi-cloud environments. The Forensic OpenStack Tools (FROST) [27] framework tackles data accessibility and ownership concerns by providing authentication and security mechanisms for digital data within OpenStack environments. However, FROST is exclusively compatible with OpenStack systems, lacking integration with major platforms like Azure or AWS. Thislimitation restricts its applicability in multi-cloud or hybrid-cloud setups, reducing its overall versatility. The Cloud Forensics Investigation Model (CFIM), introduced by [PERSON] et al. [28], addresses data ownership and accessibility challenges in cloud forensic investigations. This framework facilitates the collection and processing of evidence by taking frequent snapshots of virtual machines (VMs) to capture system states. These snapshots allow investigators to reconstruct events, gather evidence, and analyse data.