Digital Forensic Analytics

Students will be using FTK Imager, Magnet Encrypted Disk Detector, OS Forensics Volatility Workbench, Kali Linux, Reg Ripper, Network Miner, and Wireshark to develop a foundation framework for the practice of Digital investigations, including technical and managerial issues.

Students will develop the ability to write basic python scripts (WIngIDE) to help in digital investigations. Students will also interpret scripts written in other languages, like EnCase Enscript or Perl or Microsoft Powershell or UNIX Shell Script.

Students will use tools such as The Sleuth Kit and Volatility to conduct forensic analysis of PC & server operating systems and software running on those systems, while gaining a knowledge set of forensic relevant information about operating systems, including those used in mobile devices. Finally, students will look at the challenges of virtualization, including those that come with the growth of Cloud systems.

In this class, students use tools like nfdump, Snort and Wireshark for network forensic analysis in the context of the role of an incident response team.

In this course students learn to differentiate relational and non-relational databases, and to design a database to an organization's requirements. Preparing raw data for import into a database, using queries to manipulate data, and developing reports to meet stakeholder needs, and database integrity and security are also covered.

Students learn to create and use scripts to access, acquire, prepare, and transform data, including structured and semi-structured data, and to define and detect patterns in unstructured data to demonstrate how real world problems can be answered with data.

In this course students learn about deep learning, machine learning, and artificial intelligence, and compare supervised and unsupervised learning. Machine learning workflows and the challenges associated with machine learning, such as bias and heteroscedasticity, are considered. Students evaluate models, and examine uses for machine learning and artificial intelligence. Natural Language Processing is also addressed.

In this course students learn about data types and how to perform pre-processing activities including cleaning, integration, reduction, and transformation. Use of appropriate techniques to analyze and model data, such as classification, decision trees, association rules, and clustering, and the causes and methods of detecting outliers are also covered. Students propose additional applications for data mining based on current trends/research.

This course focuses on designing and implementing advanced data visualizations to interpret and communicate complex digital forensic data. Emphasizing practical applications in real-world investigations, students will create visual representations of digital evidence, including timelines, network diagrams, and geospatial data. The course enhances the students' ability to detect patterns, trends, and anomalies in digital forensic data.

This course focuses on applying advanced digital forensic techniques to investigate and analyze digital evidence from multiple sources, including operating systems, networks, and databases. Students will use scripting, automation, and data analytics to uncover patterns, interpret findings, and produce professional forensic reports.

This course challenges students to conduct digital forensic investigations, utilizing analytical techniques to interpret complex data and evidence. Students will develop professional forensic reports, applying data analytics to identify trends and anomalies. Emphasizing critical thinking, problem-solving, and clear communication, students will showcase their ability to deliver clear, compelling presentations of their findings.