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Critical Technology Studies Program

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The UTSA Intelligence Community Center for Academic Excellence can help you pursue a rewarding career in the intelligence community or national security field, providing the skills needed to tackle national security challenges head-on through:

This program, which is sponsored by the Defense Intelligence Agency and Office of the Director of National Intelligence, is designed to prepare you to pursue a career in the U.S. Intelligence Community. Learn more about the types of positions available in the Intelligence Community by visiting our Opportunities page and IntelligenceCareers.gov.

About the IC CAE Program

The UTSA-ICCAE program works in partnership with the following Texas institutions:Consortium Schools

UTSA Carlos Alvarez College of Business Critical Technology Studies Program ctsp@utsa.edu

Academic Coursework

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Master of Science in Data Analytics Program

The Master of Science in Data Analytics (MSDA) program focuses on data science and big data based business intelligence-oriented analytics algorithms, tools, techniques and technologies.

MSDA Critical Technology Studies Program allows students specialize in intelligence studies by completing specialized national security courses and engaging in a hands-on practicum.

Highlights

    • 33 credit hours
    • Finish as quickly as one year
    • Classes available after 6 p.m.
    • Hands-on practicum

MSDA CTSP track degree plan Day MSDA CTSP degree plan Evening

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Graduate Certificate in Intelligence Studies

This 12-hour graduate certificate will prepare you for a career in the U.S. Intelligence Community (IC) by providing practical and hands-on knowledge of analysis, reporting and briefing.

Individuals with business, foreign language, social science, computer science, criminal science, engineering or statistics backgrounds will benefit from this professional certificate.

Highlights

  • Program Start: Spring, Summer or Fall Semester
  • Program Requirements:
    • Approved application form
    • Official transcripts for new students
    • 12 credit hours

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Curriculum

National Security Courses
We offer six graduate-level courses on topics of national security and intelligence studies. All courses are taught by current or former employees of the national security sector.

NS 6003
The Role of U.S. Intelligence in National Security 
Required for the MSDA CTSP Track and the Graduate Certificate in Intelligence Studies. Typically offered in spring and summer semesters after 6 p.m.
Syllabus (PDF)		 NS 6223
Analytical Writing, Reporting and Briefing for the Intelligence Community
Required for the Graduate Certificate in Intelligence Studies. Typically offered in the summer after 6 p.m.
Syllabus (PDF)
NS 6233
Analytic Methods, Interpretation, Writing and Briefing of Intelligence
Required for the MSDA CTSP Track. Does not count towards Graduate Certificate in Intelligence Studies credit. Typically offered in the summer after 6 p.m.
Syllabus (PDF)		 NS 6503
Intelligence Reasoning and Analysis
Required for the Graduate Certificate in Intelligence Studies. May count towards MSDA CTSP Track credit. Typically offered in fall and spring semesters after 6 p.m.
Syllabus (PDF)
NS 6523
Methods in Intelligence Collection
Required for the Graduate Certificate in Intelligence Studies. Does not count towards MSDA CTSP Track credit. Typically offered in fall and spring semesters after 6 p.m.
Syllabus (PDF)

 

 

 

 

 

 

 

 

 NS 6723
National Security and Human-Digital Technology Relationships

May count for credit in the MSDA CTSP Track. Does not count towards the Graduate Certificate in Intelligence Studies. Typically offered in the fall after 6 p.m.

Course Description: No prerequisites. One of the recent key emerging areas of research is the role of psychological, social and cultural processes in cyber conflict. Following the kill chain upstream you will find at the end a human with motivations and objectives. This course examines a number of critical elements involved in the relationship between humans and digital technology as it relates to cyber and national security, including the role that motivations for malicious online acts and how social dynamics affect the emergence of relationships between non-nation state actors and nation states, the evolving nature of social movements and communities online and the emergence of cyberterrorism as a new entrant into the cyber threat matrix.
Research Highlights

Anomalous Behavior Detection from Multi-camera Surveillance

Abstract: Surveillance cameras are now common in schools, stores, and government buildings. The surveillance feeds generated from these thousands of cameras contain image data of vast human activity, but it is difficult to manually monitor these cameras for suspicious activity.

In this research, we seek to improve our current image detection methods by improving visualization and scenario creation in a virtual environment. We will apply AI-driven body tracking detection to synchronized surveillance feeds from multiple Azure Kinect cameras.

Expected research outcomes include detection of intruders and/or weapons from multiple cameras, body-tracking, and regeneration of these data into a virtual scene to complement real-time surveillance models.

*The UTSA IC CAE Program sponsors artificial intelligence research with national security applications, made possible with support from the Defense Intelligence Agency and Office of the Director of National Intelligence.

Anomaly Detection from System Logs

Abstract: Identifying abnormal behavior automatically to detect attacks on systems based on operational data logs could function as a powerful proactive security tool. Towards this goal, we present Anomaly Detection from System Logs using Transformer, a tool for automatically detecting anomalies in distributed execution environments.

With the massive volume of logs generated from systems, it is impossible for human operators to verify and keep track of the log files. A Transformer-based AI system detects anomalies by inspecting operational data logs and identifying abnormal behavior. Given log entries generated by a system, the anomaly detection framework parses the log entries and detects anomalies. By combining natural language-based learning models, the anomaly detection framework parses the log information stored in log files, learns normal behavior from the parsed log data and detects abnormal behaviors from new log entries.

Contact Us

Max Kilger, Ph.D.
Professor of Practice

Paul Rad, Ph.D.
Associate Professor of Information Systems and Cyber Security

Daisy Saucedo
Student Development Specialist
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