Graduate education encourages the development of advanced skills, theoretical knowledge and critical thinking skills to practice the art and science of cybersecurity techniques to defend networks and systems, analyze attacks, and conduct forensic evaluations.
Students entering the cybersecurity program must have an undergraduate degree in Computer Science, Management Information Systems, Information Technology, or a related degree with an understanding of basic software programming, network management, and systems/development operations. Professional experience may be accepted upon evaluation. Additionally, it is important for the student to be proficient in written and oral communication skills.
The master of science (MS) in cybersecurity with emphasis in artificial intelligence prepares individuals for demanding positions in public and private sectors analyzing, managing, operating, or protecting critical computer systems, information, networks, infrastructures and communications networks.
Students will be well-versed to apply their knowledge and critical thinking related to social engineering techniques, network defenses, online malware methods, critical infrastructure protection, fraud, theft, digital forensics, and threat detection.
A background on Artificial Intelligence and Machine Learning will be provided and the potential benefits of the technology in multiple areas will be described. Python will be introduced and used to resolve AI-related questions. The program will introduce Deep Learning and provide an understanding of many ways in which AI can provide support to multiple disciplines.
Learning Outcomes
Artificial Intelligence emphasis:
- Explain the role of data analytics in organizational decision making.
- Utilize current analytical languages to manage key artificial intelligence requirements.
- Explain the fundamental aspects of artificial intelligence and the potential benefits to companies and organizations.
- Develop machine learning techniques and algorithms to resolve key artificial intelligence problems.
- Implement major algorithms and statistical models related to Machine Learning to solve problems in different areas of industry.
- Utilize Deep Learning methods to address topics in multiple disciplines.