CS 562

Fall 2026 All Classes

All Classes

Advanced Topics in Security, Privacy, and Machine Learning

Credit: 4 hours.

Advanced topics in security and privacy problems in machine learning systems, selected from areas of current research such as: adversarial machine learning, differential privacy, game theory enabled defenses, robust learning methods, machine learning based cybercrime analysis, network intrusion detection, and malware analysis, and machine learning interpretation techniques.

May be repeated if topics vary. Credit is not given towards a degree from multiple offerings of this course if those offerings have significant overlap, as determined by the CS department. Prerequisite: One of CS 446 or ECE 449 and one of CS 463 or ECE 424, or equivalent courses, by consent of instructor. Additional prerequisites or corequisites may be specified each term. See section information.

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CS 562 class schedule data for fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			75418	Lecture-Discussion	SML	12:30PM -1:45PM	WF	106B8 Engineering Hall	Liao, X	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Title: Adv Top in Sec, Priv and ML Section Info: This section will primarily focus on (1) using machine learning (ML), especially Generative AI, for applications in system and network security and privacy; and (2) understanding the security and privacy challenges posed by ML-integrated systems. Example topics include using ML to build novel security defenses (e.g., detecting system vulnerability, network intrusions, and cybercrime), as well as ensuring the robustness and resilience of ML-integrated systems (e.g., protecting data privacy and securing ML-based infrastructures). Students will read, present, replicate, and discuss cutting-edge research outcomes, and work on an original research project on the relevant topics. <br/> Prerequisites: One of CS 446 or ECE 449 and one of CS 463 or ECE 424, or equivalent courses, by consent of instructor. <br/> For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
			78206	Lecture-Discussion	SPM	2:00PM -3:15PM	WF	2233 Everitt Laboratory	Wang, G	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Section Title: Mach Lrng for Sys, Netwk & Sec Section Info: This section will primarily focus on using machine learning for system, networking, and security applications. Example topics include using ML to build novel security defenses (e.g., detecting network intrusions, cybercrime, and disinformation, and performing user authentication and vulnerability analysis), launch novel attacks (e.g., privacy attacks, password guessing, deepfake-based social engineering), and support system optimizations. We will explore new research directions and seek to understand the limitations and potential risks of ML-based approaches. Students will read, present, and discuss research papers, and work on an original research project. The goal of the project is to extend machine learning techniques to new problems and produce publishable results. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.