Online MS in Cybersecurity: Courses

This course is an advanced survey of modern topics of theory, foundations, and applications of modern cryptography. One-way functions, pseudo-randomness, encryption, authentication, public-key cryptosystems, and notions of security are covered. The course also covers zero-knowledge proofs, multiparty cryptographic protocols, and practical applications.

This course covers theory and practice of network security. Topics include static packet filter, stateful firewall, proxy firewall, IDS, VPN Device, DMZs and screened subnets, networks defense components, internal network security, host hardening, configuration management, audit, human factors, and security policies. The course also covers cryptographic protocols, privacy, anonymity and various case studies.

This course entails the independent development and documentation of a substantial security-related project using techniques and/or tools learned from the program courses. The course includes periodic reporting of progress, plus a final oral presentation and written report.

This course addresses both the fundamentals and the research boundaries of algorithm design and analysis. Covered topics include: complexity of algorithms, divide and conquer techniques, greedy methods, dynamic programming, recursive backtracking, amortized analysis, graph algorithms, polynomial-time problem reduction, NP-completeness, approximation algorithms and a selected advanced topic.

This course focuses on the fundamentals of Blockchain Technology. It provides a conceptual insight into the role of Blockchains as a means for securing distributed systems, how consensus on their blocks is reached, and the new applications that they empower. It includes the technological foundations of blockchain functionalities such as distributed data structures and decision-making models, their operations, and various architectures. The course presents a brief discussion on current Smart Contract (SM) techniques and platforms, and studies their future directions, prospects, challenges, and risks. Students will learn how blockchain systems are constructed, how to interact with them, and how to design and implement secure distributed applications. Students apply the knowledge they gain by implementing a detailed blockchain system that includes transactions, blocks, cryptography, and a consensus mechanism using a popular programming language such as Java or Python.

Explores use of big data techniques to cybersecurity problems. Topics include cybersecurity, surveillance, behavioral prediction and manipulation, data sources, data collection techniques and tools, cybersecurity analytics infrastructure, machine learning and data mining, network forensics, anomaly and malware detection, security data visualization, and security dashboard design and implementation.

This course will provide students exposure to the key legal and policy issues related to cybersecurity. It includes such topics as data security laws and enforcement actions, cybersecurity litigation, anti-hacking laws, cybersecurity and corporate governance, privacy law, the Fourth Amendment, surveillance, and international cybersecurity law.

This course introduces students to the fundamental concepts and models in Artificial Intelligence that can be applied to cybersecurity data and applications. Topics include analysis of computer viruses, network traffic, financial crime investigations, clustering user activities, and adversarial learning. Students will develop a robust understanding of machine learning’s capabilities and limitations in cybersecurity contexts, including insights into how adversaries use adversarial ML models to target ML-based security systems. The course will equip students with the skills to construct their own AI-based security tools and automate their workflows through the use of AI.

This course covers methods and tools used for network programming and simulation. Covered topics include operating system support for network protocols, inter-process communication tools (such as pipes, sockets and remote procedure calls), and design of client and server sides of network protocols.

This course prepares students to develop organizational information security programs and policies that follow recognized standards, comply with all governing laws and regulations, and meet the needs of the company culture and management organization. The course covers how to perform risk management institutionally, how to manage compliance to information security requirements, and how to delegate compliance, risk, and security functions to specific roles within the organization. It also helps learners apply strategic decision-making as companies adapt to new technologies, processes, and people practices related to processing, managing, and protecting information resources.

This course teaches students to design security solutions for cloud-based platforms and operations that maintain data availability while protecting the confidentiality and integrity of information. Course topics include cloud service models, deployment methods, identity and access management (IAM) strategies, auditing and monitoring strategies, assessing and mitigating common cloud security threats, and managing compliance and regulation requirements. The course also offers hands-on experience deploying and assessing IAM controls in a cloud environment.

This course introduces students to a wide range of topics related to ethical hacking and penetration testing. The topics cover the tools and penetration testing methodologies used by ethical hackers. The course provides a thorough discussion of what and who an ethical hacker is and how important they are in protecting corporate and government data from cyber-attacks. Students will immerse in a “Hacker Mindset” in order to teach them how to think like a hacker and defend against future cyber-attacks. Students will utilize hands-on applications of security techniques by employing systematic and ethical hacking processes in a professional manner. Various tools for scanning, penetration testing, and securing target systems will be demonstrated. The five phases of ethical hacking will be presented including reconnaissance, gaining access, enumeration, maintaining access, and covering tracks.

This course will address various issues (attacks and defense strategies) in wireless and mobile security, including WEP and WPA, wireless jamming attacks, and mobile privacy. Topic coverage will include vulnerabilities, attacks, security mechanisms, and trade-offs at various layers of the network protocol stack, from aspects of physical communication to application and service security issues; examples include MAC-layer misbehavior, selective packet dropping, decentralized trust and reputation, and cross-layer holistic attacks. Systems of interest include (but are not limited to) personal devices, connected vehicles, embedded and IoT systems, wireless infrastructure, and ad hoc networks.

This course focuses on the variety of elements needed to address and implement secure software acquisition and development throughout the software development life cycle (SDLC). The course addresses people, technology, tools, and processes to design and develop consistently secure applications from start to finish. Additionally, it underscores the importance and value of the Defense-in-Depth principle across the entire SDLC. Topics covered include security in requirements engineering; secure designs; risk analysis; the SQUARE process model; threat modeling; defensive coding; fuzzing; static analysis and security assessment; memory leaks, buffer and heap overflow attacks, and injection attacks. The course also introduces techniques to adapt common security activities to modern software development practices such as Agile and DevSecOps.

This course provides an overview of popular algorithms in machine learning. Topics include supervised and unsupervised learning, linear and polynomial regression, clustering, classification algorithms, gradient descent, support vector machines, decision trees, random forests, instance-based learning, neural networks, and genetic algorithms.

This course covers several advanced topics in databases and modern data-intensive systems. Topics include advanced concurrency control techniques, query processing and optimization strategies for relational database systems, advanced indexing methods, parallel and distributed database systems, NoSQL, database-as-a-service (DB clouds), data on the web, data replication, and topics in database security and privacy.

This course exposes students to the fundamentals of IoT as a paradigm in addition to the foundational problems inherent in this realm. The course will introduce the basic terminology and ecosystem, plus development environments. Topics include IoT hardware and software platforms, data collections and analytics for IoT, security and ethical issues inherent in IoT, and networks programming for IoT. The course explores problem solving for IoT analytics based on machine learning and deep learning using TensorFlow.

This course explores the critical realm of ransomware, providing students with essential knowledge to combat this pervasive cyber threat. Topics include understanding of ransomware fundamentals, attack types, ransomware families, prevention strategies, common signs of ransomware attacks, methods for identifying potential threats, and recovery.

The course covers data management and systems aspects of big data. Topics include an overview of big data management systems, distributed big-data storage, Programming models in big data, column-based storage, analytics on big data, big spatial data, and document databases.

This course covers principles of deep learning and in its applications. Students will learn how to build and use different kinds of deep neural networks using hands-on approach. Topics include feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers and encoders/decoders. The course will include hands-on applications covering natural language processing tasks, behavioral analysis, financial analysis and anomalies detection.

This course covers word vector representations, embeddings, syntax parsing, vector space modeling, dimensionality reduction, speech tagging, text classification, sentiment analysis, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks, and large language models.

This course covers the fundamentals of reinforcement learning using a problem-based approach by addressing goal-directed problems on automated learning in an uncertain environment. Topics include finite Markov decision processes, dynamic programming, Monte-Carlo simulations, temporal-difference learning including Q-learning, function approximation, and policy gradient methods.

This course provides a theoretical foundation and practical skills for Generative AI. Topics include anatomy of generative models, transformers, GAN, the Diffusion Model for images. Students will be equipped with the skills to leverage state of the art techniques for visual representation, generative text, text to image synthesis and more.