Cloud forensics is a branch of digital forensics that deals with the collection and analysis of data from cloud environments, such as cloud storage, cloud computing, or cloud services. Cloud forensics can be used for various purposes, such as investigating crimes, resolving disputes, or ensuring compliance.
Cloud forensics encounters several challenges, including data accessibility issues due to remote servers controlled by third-party providers, potentially restricting investigator access. Data volatility is another concern, with cloud data being dynamic and subject to rapid changes or removal, affecting its reliability for forensic purposes. Additionally, the complexity of cloud data, often distributed and heterogeneous, comprising various formats across different locations and devices, increases the complexity and cost of collecting and analyzing it in forensic investigations.
To address these challenges and difficulties, researchers from the University of Texas at San Antonio (UTSA) have developed a new method for cloud forensics that can be used to collect and analyze data from cloud environments. The method uses a combination of machine learning and artificial intelligence to automate the process of collecting and analyzing cloud-based evidence.
This method encompasses two essential phases: firstly, it employs a machine learning model and web crawler to automatically identify and gather relevant data from cloud environments based on predefined criteria like keywords or timestamps; secondly, an artificial intelligence system, aided by a knowledge graph, automatically analyzes and interprets the collected data using techniques such as natural language processing, sentiment analysis, and topic modeling, providing a comprehensive and organized understanding of the data’s concepts, relations, and patterns.
This cloud forensics method offers benefits such as increased efficiency through automation, scalability for handling large datasets, and user-friendly interfaces with customization options, enhancing the forensic process.
The method is still in its early stages of development and testing, and is subject to improvement and evaluation based on further research and experimentation. The researchers have published their work in a paper titled “A Machine Learning-Based Approach for Cloud Forensics” in the journal IEEE Transactions on Information Forensics and Security. The researchers have also released a prototype of their system called CloudXaminer, which is a cloud-native tool that can be used to collect, preserve, and analyze data from various cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
