Fall 2020 Security Course  All Classes are Fully Remote

COMPSCI 461/661 Secure Distributed Systems

A course devoted to the study of securing distributed systems, with blockchain-based cryptocurrencies serving as our real platform of interest. Topics such as elliptic curve cryptography, fundamental results of distributed systems, cryptoeconomics and finance, network efficiency, and network attacks such as double spends, selfish mining, and denial of service attacks.


COMPSCI 563 Internet Law & Policy

This course is meant for those looking for legal knowledge for use in computing- and Internet-related endeavors. The course will include topics related to security, policy, and the use of machine learning and related technologies. Course material provides practical information for use by computer professionals. Some of the topics covered include: infrastructure of the Internet, basic legal principles, contract law, substantive laws, intellectual property law, ethics, dealing with third parties, policy issues, and implications of applying machine learning technology. 
 

COMPSCI 597N Introduction to Computer & Network Security

This course provides an introduction to the principles and practice of computer and network security with a focus on both fundamentals and practical information. The three topics of this course are cryptography, privacy, and network security.  Subtopics include cipers, hashes, key exchange, security services (integrity, availability, confidentiality, etc.), security attacks, vulnerabilities, anonymous communications, and countermeasures.  


COMPSCI 696E Machine Learning Applied to Child Rescue

This course is a group-based, guided independent study. Students will be encouraged to design and build their own diagnostic and machine learning tools, while also learning from professionals in the fields of digital forensics and law enforcement. The entire student group will meet once a week to share progress via short presentations. Prerequisites:  Permission of instructor only. To gain permission, you must be a graduate student in CMPSCI with a machine learning background.