Master of Science Cybersecurity

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About

The Master of Science in Cybersecurity prepares you to expand your cybersecurity hygiene and toolkit to better protect organization’s invaluable data.

You will gain the expertise to identify and mitigate risk and vulnerabilities within an organization’s computer systems. EWU’s hands-on real-world curriculum provides is aligned to the NSA Center of Academic Excellence guidelines and the NICE Framework. These guidelines and frameworks allow you to gain a deep knowledge concerning software and hardware security, a deep understanding of computer systems and network security, forensics and incident response, and cyber physical systems.

Many different organizations across a variety of sectors, including federal and local government agencies, tech companies, and many other organizations need to hire cybersecurity professionals.

Earn your M.S. in Cybersecurity to progress in your career and make an impact.

What You'll Learn

The following information comes from the official EWU catalog, which outlines all degree requirements and serves as the guide to earning a degree. Courses are designed to provide a well-rounded and versatile degree, covering a wide range of subject areas.

Cybersecurity, Master of Science (MS)

Required Core–this coursework provides core knowledge in the areas of cybersecurity research methods, advanced computer and information security, and advanced network security, engineering, and research methods.
CYBR 515RESEARCH METHODS AND COLLOQUIUM4
CYBR 525
& 525L		ADVANCED COMPUTER AND INFORMATION SECURITY
and ADVANCED COMPUTER AND INFORMATION SECURITY LAB		4
CYBR 535
& 535L		ADVANCED NETWORK SECURITY
and ADVANCED NETWORK SECURITY LAB		4
CYBR 599DIRECTED STUDY4
Choose One Concentration20
General Cyber Concentration
PROJECT MANAGEMENT FOR CYBERSECURITY
Choose four courses from the approved elective list at least three course must be at the 500-level. Note: this coursework provides the student an opportunity to take courses specialized to their particular area(s) of interest. Any 400-level or non-CYBR course must be approved by the CYBR graduate coordinator or the student’s graduate committee chair. The course CYBR 539 may each apply more than once, provided distinct topics are studied.
Secure AI Concentration
All courses must be completed for the Secure AI designation. (If the CSCD courses or the CYBR 580 course have already been completed, choose 500-level CYBR elective courses.)
MACHINE LEARNING
and MACHINE LEARNING LAB
DEEP LEARNING
and DEEP LEARNING LAB
AI FUNDAMENTALS
and AI FUNDAMENTALS LAB
AI METHODS AND VALIDATION
and AI METHODS AND VALIDATION LAB
SECURING AI
and SECURING AI LAB
Thesis or Project - A Minimum of 12 Credits are Required
Note: The student is expected to expand their knowledge with a published thesis or to apply their knowledge to a significant project. Projects may be work-related. The thesis or project is defended in a final oral examination of the student’s work.
CYBR 600THESIS12
or CYBR 601 RESEARCH REPORT
Total Credits48

Students who earn an MS in Cybersecurity from EWU should be able to:

General MS Cybersecurity Concentration

  • demonstrate cybersecurity principles in the securing of networks and software systems;
  • possess an advanced understanding of core cybersecurity knowledge;
  • use advanced cybersecurity skills in securing of networks and the development of software systems.

Secure AI Concentration

  • demonstrate cybersecurity principles in the securing of networks and software systems;
  • possess an advanced understanding of core cybersecurity knowledge;
  • use advanced cybersecurity skills in securing of networks and the development of software systems;
  • Understand the Legal and Ethical Implications: Navigate the legal and ethical considerations specific to AI security, including privacy, accountability, and the impact of AI on society;
  • Understand AI and Machine Learning Fundamentals: Grasp the core principles of AI, including machine learning, neural networks, and data science, which underpin AI systems;
  • Assess AI System Vulnerabilities: Identify and analyze potential vulnerabilities in AI models, including data poisoning, adversarial attacks, and model inversion;
  • Secure AI Pipelines: Implement security measures throughout the AI development pipeline, from data collection and preprocessing to model training, deployment, and maintenance;
  • Defend Against Adversarial Attacks: Develop strategies to protect AI models from adversarial attacks, which involve malicious inputs crafted to deceive the AI system;
  • Ensure Data Integrity: Protect the integrity and confidentiality of the data used in AI systems, ensuring that the data is free from tampering and bias;
  • Mitigate Bias and Fairness Issues: Identify and mitigate bias in AI systems to ensure fair and ethical outcomes, especially in high-stakes applications;
  • Design Robust AI Systems: Create AI systems resilient to attacks, errors, and other disruptions, ensuring reliable and secure performance in various environments;
  • Monitor AI System Performance: Monitor AI systems to detect and respond to security threats, performance degradation, and unexpected behaviors.