The Department of Computer Science and Engineering (CSE) is in the approval process of a new certificate on CyberAI: Artificial Intelligence (AI) for Cybersecurity. We aim to combine the fields of cybersecurity and AI for cybersecurity tasks like threat detection and analysis, as well as how to secure AI systems to provide a high quality, academically challenging, and career-enriching educational program that is responsive to industry trends, changing standards, and employer needs. The rapid integration of AI into cybersecurity operations has created an urgent workforce demand for professionals skilled in both domains. Government agencies, private industry, and national defense organizations increasingly rely on AI-driven solutions for threat detection, automated response, malware analysis, and large-scale security monitoring.

The Cyber AI concentration program level learning outcomes are as follows: 

  1. Understand AI and Machine Learning Fundamentals - Demonstrate mastery of core AI and machine learning concepts, including supervised and unsupervised learning, neural networks, and deep learning, as applied to cybersecurity.
  2. Implement AI-Driven Security Solutions - Design, develop, and deploy AI-based tools to improve the detection, prevention, and response to cyber threats.
  3. Automate Anomalous Behavior Detection - Apply AI techniques to identify malware, phishing attacks, intrusions, and anomalous behaviors across networks, systems, and users.
  4. Analyze Large-Scale Cybersecurity Data - Utilize AI to process and analyze large security datasets in real time for threat discovery, vulnerability detection, and trend analysis.
  5. Secure AI Systems - Address security risks unique to AI systems, including adversarial attacks, data poisoning, and model integrity.
  6. Integrate AI into Cybersecurity Infrastructure- Seamlessly incorporate AI solutions into existing security architectures and workflows.
  7. Customize AI Solutions for Industry Needs - Tailor AI-driven cybersecurity applications for specific organizations, industries, and threat environments.
  8. Apply Ethical and Legal Principles - Evaluate ethical, legal, and regulatory considerations governing the use of AI in cybersecurity.

After graduation, the CyberAI certificate owners will be prepared for jobs in secure software development, system test/evaluation, data security analysis, IT security project management, cyber defense analysis, vulnerability assessment, and system security engineering, security architecture, enterprise architecture, and scientific research positions. 

Academic standards

Students in this certificate program are required to maintain a 3.0 cumulative GPA to remain in good standing. Students who fall below 3.0 will be placed on probation the following term/semester. Students who cannot raise their GPA above 3.0 during that term/semester will be dropped from the program. Full details about the academic standards required at UNT can be found in the Academics section of the UNT catalog.

Courses

The courses provide students with a solid foundation in cybersecurity, AI, and their utilization. All students are expected to complete the following courses with at least a B to be eligible for a certificate: 

Required AI Courses

  • CSCE 5215 – Machine Learning: Covers theory and practice of designing systems that learn from data for prediction and decision-making. Topics include supervised and unsupervised learning, classification, regression, ensemble methods, and reinforcement learning.
  • CSCE 5214 – Software Engineering for AI: Introduces software engineering principles and programming paradigms for AI-based systems. Students learn to develop, maintain, and integrate AI applications using modern AI libraries, APIs, and data-driven software architectures.

Required Cybersecurity Courses

  • CSCE 5550 – Introduction to Computer Security: Fundamental principles of computer security, including threats, vulnerabilities, cryptography, system security, network security, risk analysis, and security policy development.
  • CSCE 5565 – Secure Software Development: Techniques for designing and implementing secure software systems. Topics include secure coding, software assurance, static/dynamic analysis, testing, model checking, and architectural approaches to trustworthy computing.
  • CSCE 5560 – Secure Electronic Commerce: Focuses on security in web-based and electronic commerce systems. Includes cryptography, digital signatures, PKI, certificates, legal and ethical considerations, and a required research project.