Machine Learning in Digital Forensics
Data Science is not just for Data Scientists, and Machine Learning (ML) is not just for AI Developers.
With data science, we have the maths of the past applied to data analysis. For this, engineers and scientists have been using these methods for decades, and where the term of a data scientist is a little lost on many. These days, most researchers could be defined as data scientists, as they spend a good deal of their time capturing, analyzing and presenting data. And, for ML, too, the years of it being a specialized tool for those who study ML have passed. Now, ML is a tool in every researcher’s tool kit. With a click of a button, we have access to advanced ML models and deep learning techniques.
What is required is not quite a deep understanding of ML, but core domain knowledge, as without a deep understanding of the domain, there is little chance of advancements in research. A data scientist is unlikely to uncover a new method to determine a side channel in an elliptic curve operation, but a cryptography researcher can easily apply their art with ML methods.
And, it is in cybersecurity that we possibly face one of the greatest challenges in data science. How do we detect and mitigate cyber threats, and where we are increasingly swamped with data? Nowhere is this more relevant than in digital forensics, and which has seen a…
