This course is CMPT 479/982: AI in Security. Welcome!

This is a seminar-based course. Our objective is to conduct original research. To learn to do so, we will read, discuss, and critique academic papers. We will focus on papers that use machine learning in security & privacy research.

The course will be organized as follows:

• Phase 1: We will start with around 4 weeks of lectures. The lectures will cover two major topics: Machine Learning and Security. Because these materials are significantly condensed - we are turning full courses into 1-2 weeks of material! - you are expected to pursue self-learning.
• Phase 2: We will discuss and critique papers for the rest of the semester, around 8 weeks. Finally, we will have 1 week of presentations on your projects.
  • There will be 3 papers every week, with 25 minutes for the presentation followed by 20 minutes of discussions.

Classes are held in BLU10921 on Tuesday 11:30AM - 2:20 PM.

Recordings will be available from Information Systems; the link will be posted here.

Please e-mail me if you need to meet me at other times or in person.

Grading

Your mark will consist of the following:

• Participation: 20%
  • Participation grade is given for participating in in-class discussions during Phase 2.
• Assignments: 20%
  • There will be 2 assignments that will guide you on using machine learning in security & privacy research.
• Presentation: 10%
  • Present one (or more) academic papers in Phase 2. Your highest grade will be taken.
• Project: 30%
  • Conduct original research and demonstrate your work. Group sizes TBD.
• Oral Exam: 20%
  • We will have an oral exam in the exam period. We will talk about your project and the course material.

Detailed rubrics are here.

Deadlines

Choose paper(s) to present: May 29. Link to be posted here.

Form project group in CourSys:

Assignment 1:

Assignment 2:

Project submission:

Slides (Phase 1)

Introduction

Machine Learning

Available soon:

Machine Learning - Neural Networks

Security

Notes on computer security research

Papers (Phase 2)

More papers will be added.

Week 1 - Anomaly Detection - Malware Detection

A Unifying Review of Deep and Shallow Anomaly Detection. Ruff et al. Proceedings of the IEEE, 2021.

ADBench: Anomaly Detection Benchmark. Han et al. NeurIPS, 2022.

Prudent Practices for Designing Malware Experiments: Status Quo and Outlook. Rossow et al. IEEE S&P, 2012.

Malware Detection by Eating a Whole EXE. Raff et al. AAAI Workshop on Artificial Intelligence for Cyber Security, 2018.

Week 2 - Anomaly Detection - Intrusion Detection

Toward a reliable anomaly-based intrusion detection in real-world environments. Viegas et al. Computer Networks, 2017.

MalBoT-DRL: Malware Botnet Detection Using Deep Reinforcement Learning in IoT Networks. Al-Fawa'reh et al. IEEE IoT Journal, 2024.

"Why 6?" Defining the operational limits of STIDE, an anomaly-based intrusion detector. Ran and Maxion. IEEE S&P, 2002.

Week 3 - Model Security

One pixel attack for fooling deep neural networks. Su et al. IEEE Transactions on Evolutionary Computation, 2019.

Transferable Adversarial Perturbations. Zhou et al. ECCV, 2018.

Week 4 - Biometric Systems

Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. Sharif et al. CCS, 2016.

Week 5 - Traffic Analysis

Week 6 - Traffic Analysis

Week 7 - Data Privacy

Inverting Gradients - How easy is it to break privacy in federated learning?. Geiping et al. NeurIPS, 2020.

"I know what you did last summer": query logs and user privacy. Jones et al. CIKM, 2007.

Deep learning with differential privacy. Abadi et al. CCS, 2016.

Week 8 - Science

Dos and Don’ts of Machine Learning in Computer Security. Arp et al. USENIX Security, 2022.

SoK: Science, security and the elusive goal of security as a scientific pursuit. Herley et al. IEEE S&P, 2017.

Week 9 - Presentations

Assignments

Assignments will be posted here when released.

Other resources