Special Topics

Special Topics courses cover some of our most innovative and promising research directions.  They are often prototypes of new courses that we are developing.

Special Topics courses offer variable course content, so each semester's offerings are unique.

Winter 2022

We aren't offering any courses in Winter 2022.

Spring 2022

CSE 510 Software Security (Lecture)
Section: ZHAO
Instructor: Ziming Zhao
Description: This course is designed to provide students with good understanding of the theories, principles, techniques and tools used for software and system hacking and hardening. Students will study, in-depth, binary reverse engineering, vulnerability classes, vulnerability analysis, exploit and shellcode development, defensive solutions, etc. to understand how to crack and protect native software. In particular, this class covers offensive techniques including stack-based buffer overflow, heap security, format string vulnerability, return-oriented programming, etc. This class also covers defensive techniques including canary, shadow stack, address space layout randomization, control-flow integrity, etc. A key part of studying security is putting skills to the test in practice. Hacking challenges known as Capture The Flag (CTF) competitions are a great way to do this. In this class the progress of students are evaluated by lab assignment and in-class Capture-The-Flag (CTF) competitions.
URL: https://zzm7000.github.io/teaching/2022springcse410510/index.html
Instruction Mode: In person
Class #: 24287
Dates: 01/31/2022 - 05/13/2022
Days, Time: MW, 5:00PM-6:20PM
Location: Obrian 109, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/40 (0/40 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 510LEC registration number 24287 calendar icon | Course Catalog: CSE 510LEC orange catalog icon
CSE 610 Security in Emerging Cyber Physical Systems (Lecture)
Section: HU
Instructor: Chunming Qiao
Description: A cyber physical system (CPS) typically includes a cyber subsystem with both hardware and software for sensing, computing, communications/networking, and control, and a physical subsystem used at home, and in the industries for manufacturing, medical, transportation, energy, environment, and others. Examples of CPS include but are not limited to smart appliances, smart grids, robots, and autonomous vehicles. A CPS is considered emerging if it recently started getting deployed in the real-world or is deemed promising for wide-scale deployment in the near future. The security issues surrounding such emerging systems, however, may prevent end-users from utilizing their full potential, or, even worse, may rule out the chances of their deployment in the future. Currently, these emerging systems are built based on technologies ranging from Internet of Things (IoT) and deep-learning systems to edge and 5G/Next-G systems. In this seminar course, we will discuss some of the latest work in the area of securing emerging CPS, including emerging network technologies and security (NFV, SDN, Edge, 5G/Next-G, etc.), IoT security and privacy (smart home, connected and autonomous vehicles, voice assistant platforms - Amazon Alexa and Google Assistant, etc.), and machine learning for security and privacy (adversarial attacks and defenses on deep learning, backdoor attacks and defenses on deep learning, etc.). The main goal of the special topic course is to help students understand the state of the art in a variety of security topics in emerging CPS. As a secondary goal, students will learn how to read research papers and how to communicate technical material effectively. The special topic course is suitable for students who have a strong interest in network and system security and intent to pursue a career in the area, e.g., Ph.D. students already working in cybersecurity or MS students interested in pursuing a Ph.D. or doing research in the field (in the form of independent studies and/or MS Thesis). One of the goals of this seminar is to identify, by the end of the semester, a set of open research problems on which students can work during the next semester, e.g., in the form of independent studies.
Instruction Mode: In person
Class #: 24625
Dates: 01/31/2022 - 05/13/2022
Days, Time: TR, 12:00PM-1:20PM
Location: Davis 113A, North Campus
Credit Hours: 3.00
Enrollment: 0/30 (0/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 610LEC registration number 24625 calendar icon | Course Catalog: CSE 610LEC orange catalog icon
CSE 701 Neurosymbolic Artificial Intelligence (Seminar)
Section: A
Instructor: Sargur N. Srihari
Description: Today's successful Artificial Intelligence models are largely based on neural models which draw their inspiration from networks in the biological brain and implemented using deep learning. They have eclipsed the knowledge-based approach in which the world is represented in the form of pre-determined symbols with inference based on logic and probabilistic reasoning. However the symbolic approach can better address current limitations of deep learning, e.g., adaptability, generalizability, robustness, explainability, abstraction, common sense, causal reasoning, etc. The use of both approaches in the same system has cognitive support. Such as fast and slow thinking, wherein deep learning plays the role of fast thinking and the symbolic approach plays the role of slow thinking. The seminar course covers cognitive theories of fast and slow thinking, robust artificial intelligence, parallel and sequential use of deep learning and causal reasoning and implementation issues such as attention and co-operating mulitagents. We will study current papers on these topics.
Notes: This seminar was first offered in Spring 2021. Topics covered at that time can be found at https://cedar.buffalo.edu/~srihari/CSE701/index.html. The 2022 edition will cover papers that have since appeared. Seminar participants will be expected to study one such paper and share their understanding with the others. There will be no compulsory projects or tests.
Prereqs: CSE 4/574
URL: https://cedar.buffalo.edu/~srihari/CSE701/index.html
Instruction Mode: In person
Class #: 22041
Dates: 01/31/2022 - 05/13/2022
Days, Time: W, 9:30AM-11:30AM
Location: Davis 338A, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/30 (0/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 701SEM registration number 22041 calendar icon | Course Catalog: CSE 701SEM orange catalog icon
CSE 702 Introduction to Digital Media Forensics (Seminar)
Section: A
Instructor: Siwei Lyu
Description: The widespread adoption of digital content over traditional physical media such as film has given rise to a number of new information security challenges. Digital content can be altered, falsified, and redistributed with relative ease by adversaries. This has important consequences for governmental, commercial, and social institutions that rely on digital media. The pipeline which leads to ascertain whether an image, audio, or video has undergone some kind of forgery leads through the following steps: determine whether the content is "original" and, in the case where the previous step has given negative results, try to understand the past history of the content. Although the field of multimedia forensics is still young, many forensic techniques have been developed to detect forgeries, identify the origin, and trace the processing history of digital multimedia content. This course provides an overview of information forensics research and related applications. Also, we examine the device-specific fingerprints left by digital image and video cameras along with forensic techniques used to identify the source of digital multimedia files. Finally, an overview of the recent trends and evolution, considering the updated literature in the field, will be provided.
Prereqs: Machine Learning (CSE 4/574: Intro to ML) or equivalent. Signal Processing or Computer Vision (CSE )
URL: https://cse.buffalo.edu/~siweilyu/DMF_class.html
Instruction Mode: In person
Class #: 21933
Dates: 01/31/2022 - 05/13/2022
Days, Time: R, 8:10AM-10:50AM
Location: Baldy 200G, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/30 (0/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 702SEM registration number 21933 calendar icon | Course Catalog: CSE 702SEM orange catalog icon
CSE 705 Fast Algorithms for Graph Analytics (Seminar)
Section: SARI
Instructor: Ahmet Erdem Sariyuce
Description: Graphs are everywhere. Their scale, rate of change, and the irregular nature pose many new challenges. Deep learning has been shown to be successful in a number of domains, ranging from images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics. This seminar course covers recent papers in the last few years about deep learning on graphs. We will consider graph embeddings, knowledge graphs, graph kernels, graph neural networks, graph convolutional networks, graph adversarial methods. Students will learn the literature on deep learning on graphs, understand the state-of-the-art algorithms on various problems, and be familiar with the recent trends.
Notes: (The website is from last year and there'll be changes to the grading policy and the paper list)
Prereqs: It is assumed that students have a solid background on discrete mathematics and algorithms. Basic research skills like paper reading, critical thinking, problem solving, report writing, communication, and presentation are important as well.
URL: https://sariyuce.com/F20-701.html
Instruction Mode: In person
Class #: 24193
Dates: 01/31/2022 - 05/13/2022
Days, Time: W, 10:00AM-12:40PM
Location: Grein 134C/135C, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/30 (0/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 705SEM registration number 24193 calendar icon | Course Catalog: CSE 705SEM orange catalog icon
CSE 706 Emerging Biometrics and Mobile Authentication (Seminar)
Section: JIN
Instructor: Zhanpeng Jin
Description: This seminar course concentrates on the discussion of emerging biometrics and user authentication technologies on mobile and wearable devices. Given the increasing advances of mobile and wearable technologies in people’s daily life, many new biometrics and authentication techniques are being explored and developed. Specifically, this seminar will review, discuss, and characterize a set of emerging biometrics (e.g., behavioral patterns, user profiling, heart beats, brain activity, etc.). The seminar will also explore recent research efforts and commercially available techniques on mobile user authentication and discuss their applicability and limitations (e.g., fingerprint, face, iris, and voice, etc.). By the end of the seminar, students will learn how to evaluate (i.e., performance metrics) and design user authentication systems using biometrics. The following topics will be covered: 1. Emerging biometrics (physiological, bio-signals, behaviors, etc.). 2. Mobile user authentication techniques. 3. Comparison and evaluation of all available technologies. 4. Brainstorming and exploration of possible future biometric and authentication approaches.
Notes: This class will need a general background (and interest) in biometrics, mobile computing, cyber physical security, and Internet-of-Things (IoTs).
Prereqs: None
Instruction Mode: In person
Class #: 17405
Dates: 01/31/2022 - 05/13/2022
Days, Time: F, 10:00AM-12:40PM
Location: Park 440, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/30 (0/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 706SEM registration number 17405 calendar icon | Course Catalog: CSE 706SEM orange catalog icon
CSE 710 Selected Topics in Computer Vision (Seminar)
Section: YUAN
Instructor: Junsong Yuan
Description: As a key research area of artificial intelligence (AI), Computer vision has great progress in the past decade, and is now providing exciting solutions for many real world problems, such as autonomous driving, video surveillance, e-commerce, robots. This seminar is a dive into the state-of-the-arts in computer vision, with a focus on deep learning and visual analytics. The course will be beneficial to students interested in understanding the recent literature in computer vision.
Notes: Grading Only satisfactory (S) and unsatisfactory (U) will be given. Students taking one credit requires to complete a survey of a specified computer vision topic. Students taking two credits need to complete a course project. Students taking three credits need to complete both a research survey and a course project. Project representation will be on the final two weeks. All students are required to attend the weekly seminar and participate the discussions on class.
Prereqs: Students should be very familiar with programming in python, and have taken the following courses before joining this seminar. Students should have background knowledge in both computer vision and deep learning. CSE 473/573 Computer Vision and Image Processing CSE 474/574 Introduction to Machine Learning CSE 410 Introduction to Deep Learning (or equivalent)
Instruction Mode: In person
Class #: 24197
Dates: 01/31/2022 - 05/13/2022
Days, Time: W, 7:00PM-9:40PM
Location: Unknown, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/30 (0/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 710SEM registration number 24197 calendar icon | Course Catalog: CSE 710SEM orange catalog icon
CSE 713 Wireless Networks Security, Principles and Practices (Seminar)
Section: UPAD
Instructor: Shambhu J. Upadhyaya
Description: The course includes several instructor presentations and student presentations. Further, students can investigate research problems or engage in projects - simulation based or hands-on experiments. Topics included are: Overview of Security Issues in Wireless Networks, WEP Security, WPA and RSN, Bluetooth Security, Security of MANETs, Security of Sensor Networks, Wireless Mesh Networks and Security, Trust in Wireless Networks, Vehicular Networks Security, Smart Grid Security and Security of Internet of Things (IoT). Most of the topics will be from research papers and Internet documents. Topics will be assigned to or selected by students who are required to study them, prepare presentations and discuss and critique them in the class.
Notes: The course webpage will be at: http://www.cse.buffalo.edu/~shambhu/cse71322/ and will be launched end of December.
Prereqs: Basic knowledge of computer security and networking concepts
Coreqs: None
URL: http://www.cse.buffalo.edu/~shambhu/cse71322/
Instruction Mode: In person
Class #: 20719
Dates: 01/31/2022 - 05/13/2022
Days, Time: T, 11:00AM-1:40PM
Location: Clemen 103, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/15 (0/15 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 713SEM registration number 20719 calendar icon | Course Catalog: CSE 713SEM orange catalog icon
CSE 715 Special Topics in Biometrics and IoT Security (Seminar)
Section: A
Instructor: Wenyao Xu
Description: This seminar course concentrates on the discussion of emerging biometrics and IoT security. After reviewing traditional biometrics, a set of emerging biometrics (e.g., soft biometrics, behavioral patterns, etc.) and IoT security technologies will be in-depth discussed. Also, the seminar will discuss recent research work on mobile user authentication, particularly focusing on biometrics-based approaches. By the end of the seminar, students will learn how to evaluate (i.e., performance metrics) and design user authentication systems using biometrics and IoT technologies.
Notes: If you are interested in biometrics, user identification and pattern recognition, this seminar is highly recommended.
Instruction Mode: In person
Class #: 22160
Dates: 01/31/2022 - 05/13/2022
Days, Time: M, 4:10PM-6:50PM
Location: Norton 213, North Campus
Credit Hours: 1.00-3.00
Enrollment: 0/30 (0/30 seats reserved for computer science & engineering majors only) (Active)
Links: Registration: CSE 715SEM registration number 22160 calendar icon | Course Catalog: CSE 715SEM orange catalog icon