Overview What You Will Learn Curriculum Instructor

Course Overview

Digital forensics is a rapidly evolving field, and staying up-to-date with the latest technology is essential for success. This Machine Learning for Digital Forensics short course is designed to provide attendees with a comprehensive overview of machine learning and how it can be applied to Digital Forensic Investigations. 

Attendees will learn the differences in supervised and unsupervised machine learning models and the algorithms that support them. The course will compare and discuss use cases for a range of learning algorithms including Regression Algorithms, Decision Trees, Naïve Bayes, SVM (Support Vector Machines) and KNN (K-Nearest Neighbors). Through hands-on practical examples attendees will learn how to apply these algorithms to leverage the power of machine learning for digital forensic tasks. 

The course will also consider the potential value of ChatGPT for digital forensic investigations and attendees will learn about the limitations of machine learning algorithms and methods for assessing their performance and accuracy. By the end of the course, attendees will have a solid understanding of the fundamentals of machine learning and how it can be used to improve digital investigation workflow efficiency without compromising on accuracy of results.

What You Will Learn

  • The challenges in modern digital forensics and the importance of optimising workflows to get to the evidence faster.
  • The algorithms and models machine learning provides to better analyse our data varied data sources.
  • The tools and processes necessary for working with machine learning models.
  • The practical steps necessary to create, test and deploy your own machine learning models.
  • The application of ChatGPT for digital forensic tasks.
  • The practical use cases for utilising machine learning models to optimised digital forensic workflows.

Program Curriculum

  • Key Aspects of Digital Forensics
  • Key Aspects of Machine Learning
  • The Need for Machine Learning in Digital Forensics
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • Introduction
  • Term Definitions
  • Machine Learning Workflows
  • Machine Learning Algorithms
  • Machine Learning Algorithms for Digital Forensic Use Cases
  • Chapter 2 Quiz

  • Section Introduction
  • Python Introduction
  • Basic Python Concepts
  • Building your First Script
  • Chapter 3 Quiz

  • Section Introduction
  • Digital Forensics Use Case Email Classification
  • Python Library Imports
  • Data Collection and Processing
  • Feature Extraction and Model Training
  • Model Testing and Validation
  • Chapter 4 Quiz

  • Section Introduction
  • Digital Forensics Use Case Email Classification
  • Python Library Imports
  • Data Collection and Processing
  • Feature Extraction and Model Training
  • Model Testing and Validation
  • Digital Forensics Use Case Graphic Image Classification
  • Digital Forensics Use Case Malicious URL Log Classification
  • Section Conclusion
  • Chapter 5 Quiz

  • Section Introduction
  • The Rise of LLMs
  • ChatGPT in Digital Forensics
  • ChatGPT the Digital Detective
  • Investigation Review
  • Chapter 6 Quiz

  • Section Introduction
  • Barrier to Entry
  • Accuracy of Results
  • Explainability and Repeatability
  • Ethical & Legal Considerations
  • Chapter 7 Quiz

  • Section Introduction
  • Summarising the Course
  • Section Conclusion
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Instructor

James Billingsley

James Billingsley is a seasoned Examiner, Consultant, Trainer and Speaker in Digital Forensics, Incident Response and Information Security, with over 15 years of professional experience. James is the co-author of forensic software tools, which focus on Internet Browser forensics, and are used globally by a number of law enforcement agencies and international corporations. James sees mastering the application of machine learning as a critical skill to develop in helping investigators triage and process data more efficiently, ensuring they can continue to get to the evidence faster as data quantities continue to grow exponentially.

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