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With new AI-based technologies that power almost all activities in the digital world, cybercrime is on an unprecedented increase. Forensics is the science of tracing causes, methods and perpetrators from evidence, once a crime has been committed. This course will teach you some of the key technologies that are being used to track cybercriminals.

Instructor and TAs

Instructor: Rita Singh ([email protected])

TAs:
Zhenzhen Liu ([email protected])\ Shahan Ali Memon ([email protected])\ Bhiksha Raj ([email protected])\

IMPORTANT INFORMATION: THIS COURSE IS NOT BEING TAUGHT IN SPRING 2022. I WILL BE SHIFTING IT TO FALL 2022. THERE MAY BE AN ALTERNATE COURSE PUT UP BY THE DEPARTMENT UNDER THE SAME NUMBER (AND POSSIBLY TITLE), WITH A DIFFERENT INSTRUCTOR, BUT THAT WILL NOT BE THIS COURSE OR SYLLABUS

Venue and timings for lectures and office hours

Basic course structure

There will be 14 weeks of lectures. A different forensic subarea will be covered each week in 2 lectures of 1 hr 20 mins each. There will be one quiz at the end of each week (released Thursday midnight, due Sunday midnight) and 4 homeworks in all. Each homework will be due within 2 weeks from the date of release.

Schedule of lectures (The order of topics, is tentative, and may change later):

Week 1: Introduction\ Week 2: Network Forensics\ Week 3: Dark Web Forensics (HW1 released)\ Week 4: Computer Forensics\ Week 5: Artificial Intelligence, Machine Learning and Deep Learning\ Week 6: Artificial Intelligence, Machine Learning and Deep Learning (HW2 released)\ Week 7: Text and Social Media Forensics\ Week 8: Audio Forensics\ Week 9: Image Forensics (HW3 released)\ Week 10: Video Forensics\ Week 11: Steganography\ Week 12: Cryptography (HW4 released)\ Week 13: DeepFakes: generation and tracking\ Week 14: Class Presentations

A more comprehensive syllabus can be found here.

The weekly lectures will be slide-based. Research papers, (accessible) textbook chapters and notes will be provided as links where necessary. Slides for each class will be uploaded on Canvas after each class, on the same day as the class. Homeworks will be released in class, and explained in a 30 minute recitation at the end of the corresponding class. Attendance is expected and recommended. Lectures will be recorded.

Enrollment requirements

You must know programming (preferably Python). Basic skills in maths, statistics and probability are expected.