Graduate Certificate in Cyber Security

About: Protecting information systems is key to protecting the nation's critical infrastructures. Only through diligence and a well-trained workforce will we be able to adequately defend the nation's vital information resources. Cyber Security has one of the largest demands for an educated workforce within the federal, state, and industry domains.

This certificate meets a majority of the requirements for the National Initiative for Cyber Security Education (NICE) standards for the NSA-DHS Center of Academic Excellence program.

Term: 1 to 3 years to graduate

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  • Requirements
  • Course Information

Requirements

Graduate Certificate Requirements:

  • Certificate programs require the completion of twelve credit hours (four designated courses) of 3000-, 4000-, 5000-, and 6000-level lecture courses (1000/2000-level courses cannot be included).

Course Information

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Required Courses

Description

An overview of information security operations, access control, risk management, systems and application life cycle management, physical security, business continuity planning, telecommunications security, disaster recovery, software piracy, investigations, ethics and more. There will be extensive reporting, planning and policy writing.

Learning Objective

  1. Security Program Leadership: Equip students with the skills to lead and manage comprehensive security programs within organizations.
  2. Incident Response Expertise: Train students in effective security operations and incident response strategies to mitigate and manage cyber threats.
  3. Compliance and Risk Management: Provide knowledge in compliance frameworks and risk assessment, enabling students to ensure regulatory adherence and minimize security risks.

Course Content

  • Security Program Leadership: Governance, policies, and awareness.
  • Incident Response: Detection, planning, and digital forensics.
  • SOC Management: Security operations center, logs, and threat intelligence.
  • Risk Assessment: Identifying and mitigating security risks.
  • Compliance and Regulations: Industry-specific compliance and auditing.
  • Security Metrics: Measuring security effectiveness and reporting.
  • Resource Management: Budgeting, resource allocation, and vendor selection.
  • Policy and Documentation: Developing policies and incident response plans.
  • Awareness and Training: Security awareness and employee training.
  • Program Evaluation: Continuous improvement and maturity assessment.

Course Evaluation Criteria

  • HWs
  • Project
  • Midterm Exam

Description

Introduces fundamentals of modern cryptography. Topics include basic number theory, public & private key encryption schemes, cryptographic hash functions, message authentication codes, elliptic curve cryptography, Diffie-Hellman key agreements, digital signatures, PUFs, quantum cryptography, and generation of prime numbers and pseudo-random sequences.

Learning Objective

  1. Fundamental Understanding: Provide students with a foundational knowledge of cryptography, its principles, and applications.
  2. Security Awareness: Raise awareness of cryptographic techniques and their role in safeguarding information in various contexts.
  3. Hands-On Skills: Equip students with practical skills for applying cryptographic methods in real-world scenarios. 

Course Content

  • Cryptography Basics: Historical context, key concepts, and cryptographic goals.
  • Types of Cryptography: Symmetric, asymmetric, and hash functions.
  • Cryptographic Protocols: SSL/TLS, SSH, and VPNs.
  • Applications: Data encryption, digital signatures, and password hashing.
  • Cryptanalysis Basics: Understanding attacks and key considerations.
  • Public Key Infrastructure (PKI): Role of Certificate Authorities and key exchange.
  • Real-World Use Cases: Email encryption, blockchain, and compliance.
  • Hands-On Exercises: Practical application of cryptographic techniques.

Course Evaluation Criteria

  • HWs
  • Project
  • Midterm Exam

Choose Two

Description

The course presents various vulnerabilities and threats to information in cyberspace and the principles and techniques for preventing and detecting threats and recovering from attacks. The course deals with various formal models of advanced information flow security. A major project will relate theory to practice.

Learning Objective

  1. To attain a comprehensive understanding of cybersecurity threats, encompassing a broad range of vulnerabilities and risks in cyberspace.
  2. To master the principles and techniques for preventing, detecting, and responding to cybersecurity threats effectively, equipping students with practical cybersecurity skills.
  3. To develop expertise in cyberattack recovery strategies, including incident response and resilience planning.
  4. To gain proficiency in advanced information flow security models, enabling the analysis and design of secure information systems.
  5. To apply cybersecurity knowledge in practical scenarios, bridging the gap between theory and real-world cybersecurity challenges.

Course Content

  • Cybersecurity Threat Landscape: Vulnerabilities and Risks
  • Principles of Threat Prevention and Detection in Cyberspace
  • Cyberattack Recovery Strategies and Incident Response
  • Advanced Information Flow Security Models and Formal Approaches
  • Practical Application of Cybersecurity Knowledge
  • Emerging Technologies and Cybersecurity: AI, IoT, and the Future of Digital Defense

Course Evaluation Criteria

  • HWs
  • Project
  • Final Exam

Description

This course covers basic tools, in statistics and cryptography, commonly used to design privacy-preserving and secure protocols in a distributed environment as well as recent advances in the field of privacy-preserving data analysis, data sanitization and information retrieval.

Learning Objective

  1. Privacy Protection: Learn how to analyze data while safeguarding individual privacy and complying with regulations.
  2. Data Integration Skills: Master combining diverse data sources for insights, even with sensitive data.
  3. Advanced Analysis: Gain expertise in extracting valuable knowledge while preserving privacy.

Course Content

  • Privacy Basics: Understand privacy laws, risks, and anonymization techniques.
  • Data Harmonization: Normalize and preprocess various data types.
  • Secure Data Sharing: Use encryption and privacy-preserving protocols.
  • Privacy-Preserving Analytics: Explore secure data mining and federated learning.
  • Tools and Technologies: Work with privacy-preserving tools and consider ethical implications.
  • Real-World Applications: Apply skills to healthcare, finance, and more through practical projects.

Course Evaluation Criteria

  • HWs
  • Projects
  • Midterm Exam

Description

Covers facets of cloud computing and big data management, including the study of the architecture of the cloud computing model with respect to virtualization, multi-tenancy, privacy, security, cloud data management and indexing, scheduling and cost analysis; it also includes programming models such as Hadoop and MapReduce, crowdsourcing, and data provenance.

Learning Objective

  1. To develop a comprehensive understanding of cloud computing architecture, encompassing virtualization, multi-tenancy, privacy, security, and data management principles.
  2. To acquire proficiency in cloud data management and indexing techniques, enabling efficient data handling within a cloud environment.
  3. To master cost analysis and optimization strategies specific to cloud computing, allowing for effective cost management and resource optimization.
  4. To achieve proficiency in programming models such as Hadoop and MapReduce, facilitating the application of these models for big data processing in cloud environments.
  5. To explore the concepts of crowdsourcing and data provenance, recognizing their significance in cloud-based big data applications.
  6. To apply cloud computing and big data tools effectively in practical scenarios, gaining hands-on experience in solving real-world problems and applications.

Course Content

  • Cloud Computing Architecture and Principles
  • Virtualization, Multi-Tenancy, and Security in the Cloud
  • Cloud Data Management and Efficient Indexing
  • Cost Analysis and Optimization Strategies in Cloud Computing
  • Programming Models: Hadoop and MapReduce
  • Crowdsourcing, Data Provenance, and Their Role in Cloud-Based Big Data

Course Evaluation Criteria

  • HWs
  • Project
  • Final Exam

Description

Topics covered include network security issues such as authentication, anonymity, traceback, denial of service, confidentiality, forensics, etc. in wired and wireless networks. Students will have a clear, in-depth understanding of state of the art network security attacks and defenses.

Learning Objective

  1. Security Mastery: Equip students with advanced knowledge and skills to protect networks from sophisticated cyber threats.
  2. Incident Response: Train students in proactive threat detection and rapid incident response techniques.
  3. Network Resilience: Teach students how to design and implement resilient network security architectures to minimize vulnerabilities.

Course Content

  • Emerging Threats: Analyze new cyber threats and intelligence sharing.
  • Security Protocols: Explore encryption, segmentation, and network access control.
  • Firewalls and IDS/IPS: Advanced firewall rules, intrusion detection, and prevention.
  • Secure Network Design: Security in network architecture, VPNs, and remote access.
  • Threat Detection and Analysis: Behavioral analysis, malware analysis, and SIEM tools.
  • Incident Response: Planning, forensics, and business continuity.
  • Cloud and Virtual Security: Securing virtualized and cloud environments.
  • Network Resilience: Building resilient network architectures and redundancy.
  • Security Policies and Compliance: Compliance, policies, and security audits.
  • Practical Scenarios: Hands-on labs, case studies, and security assessments. 

Course Evaluation Criteria

  • HWs
  • Project
  • Midterm Exam