Big data can therefore be defined as the enormous volume of structured or unstructured data kept in various storage systems, typically in the magnitude of zettabytes or petabytes, and that can be analyzed to create datasets providing patterns and interdependency of data.
Big Data Challenges in Digital Forensic
Digital forensics has found big data to be particularly difficult to analyze data on the three big data criteria of volume, variety, and velocity. Without a doubt, it can be challenging to analyze large amounts of data that are being processed quickly and without knowing from what device or source they are coming.
Additionally, it would be challenging for digital forensic investigators to deduce complex dependency and arrangement from this data, which would encourage cybercrime.
Solution – Metadata
Metadata, often known as data on data, can help solve the problem of data volume. Metadata comes in two types:
- Descriptive Metadata
- Structural metadata
For instance, what if a database had information about a photograph, such as the location, the time, and the subject matter? The information filled in these details is the structured metadata, such as the place in New Delhi, the time 12 o'clock, and the picture of Qutub Minar, which would be different for every picture. On the other hand, the descriptive metadata for the picture would stay common for all pictures.
Descriptive metadata can be used initially, and then a structured metadata database can be built from it, making it a little less difficult to manage massive amounts of data. Join Learnbay’s data science course to gain in-depth knowledge of big data concepts.
How can data science be applied?
Digital forensics can use data science to combat big data since it is virtually hard for humans to analyze such enormous amounts of data and recognize the complex patterns across the material. For instance, information can be gathered through databases, evidence, and cases that have already been resolved. The appropriate algorithms established by the rules examined on the provided data can then be derived using this data. However, the ultimate selection should be made manually to prevent making the wrong decision during the initial test phases.
- The use of predictive policing is one example. It describes how law enforcement uses mathematical, predictive, and analytical approaches to spot probable criminal activities. It was one of the best innovations since it may prevent crime from happening in
- The first place. However, it had the disadvantage of discriminating against a group of individuals and frequently making poor decisions.
Neural networks and MapReduce are a couple of the data science techniques that can be used. Apache Hadoop uses the MapReduce programming approach, a set of open-source software tools, to process large amounts of data. Several computers joined together as nodes disseminate the vast volume of data from the source in this system to shorten the time needed to store data.
Depending on the application, these nodes then reproduce the data. Many prestigious businesses, including Netflix, use Hadoop and Big Data analytics to save $1 billion annually.
In contrast, neural networks are a network of connected neurons that use mathematical calculations to make decisions. To master Hadoop and other big data tools, there are the best data science courses in India, where you can gain experiential knowledge.
However, some well-established digital forensics principles must be changed to include these modifications, such as giving up repeatability.
Additionally, many changes must be made to the workflow of digital forensics, including those in the data analysis step, where data science is necessary to assess algorithms, and the reporting step, where a precise evaluation of all the tools and techniques used in the case must be completed and recorded for future predictions.
The total data generated worldwide is predicted to reach 163 zettabytes by 2025. This can be seen as a benefit for businesses since they can employ big data analytics and data science to drive their economies more than before. However, digital forensics will also face challenges because the current tools are not equipped to handle such big data. Digital forensics must expand on its already established principles and research new tools and technologies for analysis purposes to keep up with big data analytics and data science.