Before the advent of large storage devices, data could only be saved on computers (hard drives), making it very simple for us to analyze or abstract information from this data. However, as technology has advanced, there has been an exponential growth in the amount of data that can be stored, from portable sources like USB sticks and SD cards to scattered sources like Cloud Services. 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 in Digital Forensics – Challenges
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 from.
Additionally, it would be challenging for digital forensic investigators to deduce intricate dependency and arrangement from this data, which would encourage cybercrime.
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 recognise 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 choice during the initial test phases.
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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.
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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. For further information on neural networks and mapreduce, head to the Data Science certification course in Delhi, taught by industry experts.
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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.
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In contrast, neural networks are a network of connected neurons that use mathematical calculations to make decisions.
However, to include these modifications, some well-established digital forensics principles must be changed, such as giving up repeatability.
Additionally, there are a number of changes that 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.
Conclusion
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 therefore expand on its already established principles and conduct research on new tools and technologies for investigative purposes in order to keep up with big data analytics and data science. To learn more about data science and big data technologies, visit the IBM-accredited Data Science course in Delhi, right away!
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