Building Resilient Digital Forensic Frameworks for NoSQL Database: Harnessing the Blockchain and Quantum Technology

Digital forensics is the process of gathering, examining, and presenting digital evidence from devices like computers, smart phones, and cameras with supporting documentation. Investigating cybercrimes, retrieving deleted data, confirming the veracity of documents and photos, and locating the source and location of digital information are just a few of the many uses for digital forensics. The process of investigating logs of database systems and metadata to search for clues and signs of criminal activity or security breaches is called database forensics. Forensic experts could recover lost or damaged data with tools of database forensics and determine the origin, nature, and scope of an attack. Two cutting-edge technologies that can be employed in digital forensics are blockchain and quantum computing. While quantum computing can be used to find patterns and encrypt data, blockchain can be used to guarantee the accuracy of logs and monitor data flow. These technologies offer verifiable audit trails, an unbreakable chain of custody, and secure storage of forensic artifacts for forensic investigators. Digital forensic professionals must develop quantum-safe digital evidence preservation techniques and deal with other issues brought on by quantum computing. However, with careful planning and preparation, these difficulties are surmountable. For the wide-column store NoSQL database, we provide a six-phase forensic investigation framework in this chapter. Our system includes the full process of forensic examination, from preparation to reporting, in contrast to other studies that concentrated on particular parts of NoSQL forensics, such as transaction log analysis or deleted data recovery. We also discuss the difficulties associated with finding and evaluating distributed evidence in a NoSQL setting, which is distinct from a relational DBMS. Our system may be used with a variety of documents and wide-column NoSQL DBMSs including MongoDB, CouchDB, and Cassandra. We do a case study using Cassandra as an illustration to show its efficacy.