Cybersecurity Course Schedule Spring 2020

This course explores the most important elements beyond technology that shape the playing field on which cybersecurity problems emerge and are managed. The course emphasizes how ethical, legal, and economic frameworks enable and constrain security technologies and policies. It introduces some of the most important macro-elements (such as national security considerations and interests of nation-states) and micro-elements (such as behavioral economic insights into how people understand and interact with security features). Specific topics include policymaking, business models, legal frameworks, national security considerations, ethical issues, standards making, and the roles of users, government, and industry.

This course focuses on both mathematical and practical foundations of cryptography. The course discusses asymmetric and symmetric cryptography, Kerckhoffs’ Principle, chosen and known plaintext attacks, public key infrastructure, X.509, SSL/TLS (https), and authentication protocols. The course will include an in-depth discussion of many different cryptosystems including the RSA, Rabin, DES, AES, Elliptic Curve, and SHA family cryptosystems. This course also introduces advanced topics of applied cryptography, including a brief introduction to homomorphic encrypted computation and secure multi-party computation to protect sensitive data during arbitrary computation, cryptocurrency and its cryptographic building blocks, and quantum computing.

The course presents the challenges, principles, mechanisms and tools to make software secure. We will discuss the main causes of vulnerabilities and the means to avoid and defend against them. The focus is on secure programming practice, including specifics for various languages, but also covering system-level defenses (architectural approaches and run-time enforcement). We will also apply software analysis and vulnerability detection tools in different scenarios.

Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning, with a focus on applications and concepts relevant to cybersecurity. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important.

Introduction to networking and security as applied to networks. Exercises cover network programming in a language of the student's choice, understanding and analyzing packet traces using tools like wireshark and mitmproxy, as well as applying security principles to analyze and determine network security. After this course, the student will have a fundamental understanding of networking, TLS and security as it applies to networked systems.

This survey of operating system security compares approaches to security taken among several modern operating systems. The course will teach how to conceptualize design issues, principles, and good practices in securing systems in today’s increasingly diverse and complex computing ecosystem, which extends from things and personal devices to enterprises, with processing increasingly in the cloud. We will approach operating systems individually and then build on them so that students learn techniques for establishing trust across a set of interoperating systems.

Security and privacy systems can be made more usable by designing them with the user in mind, from the ground up. In this course, you will learn many of the common pitfalls of designing usable privacy and security systems, techniques for designing more usable systems, and how to evaluate privacy and security systems for usability. Through this course, you will learn methods for designing software systems that are more secure because they minimize the potential for human error.

This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms.

This capstone course will cement skills and knowledge learned throughout the Master of Information and Cybersecurity program: core cybersecurity technical skills, understanding of the societal factors that impact the cybersecurity domain and how cybersecurity issues impact humans, and professional skills such as problem-solving, communication, influencing, collaboration, and group management – to prepare students for success in the field. The centerpiece is a semester-long group project in which teams of students propose and select a complex cybersecurity issue and apply multi-faceted analysis and problem-solving to identify, assess, and manage risk and deliver impact.