SNCA GAANN Fellowship Program

To be eligible for the SNCA GAANN Program:

• You must be a resident of the United States (U.S. Citizen or Permanent Resident only) to apply to the SNCA Program.
• You must either be a current PhD student enrolled in the Computer Science and Information Systems (CSIS) program at the University of Colorado Denver, or in the process of applying for admission to this program.

To be selected for the fellowship, applicants must show an excellent academic record in computer science-related majors, especially in areas of - but not limited to - security and privacy, data science, machine/deep learning, and network science.

To receive this fellowship, an individual must also show evidence of financial need. An individual's financial need will be evaluated and determined by CU Denver Financial Aid and Scholarship Office . Applicants must submit their FAFSA application prior to this evaluation. 

Detecting, characterizing, and countering the sophisticated cyber threats that are enabled through social networks have proven to be very challenging their variety in terms of agenda, messaging and targeting tactics and techniques, and the high volume, velocity, and variety of data that must be analyzed for their identification and characterization within one platform or across multiple platforms, and lack of effective analytical frameworks for timely, flexible and high-resolution sensemaking, detection, and characterization of these threats.

This rise in the scale and variety of advanced social engineering threats on social networks, fueled by the lack of effective defensive tools and methods against those has, more than ever before, engaged United States citizens in an asymmetric, continual, war of disinformation and weaponized influence narratives. Reversing the dynamics of this cyber warfare entails a vital need to build a cybersecurity program focused on this emerging science of Social Networks Cybersecurity Analytics aiming at raising national capacity and capability in addressing influence campaigns, coordinated and personalized social engineering attacks, disinformation, and misinformation propaganda, and the wide array of emerging threats that are either enabled or facilitated through social networks. Achieving this grand objective requires an inter-disciplinary effort by scientists from a variety of domains, including computer sciences (cybersecurity, data science, machine/deep learning), information sciences, information theory, graph theory, as well as collaboration with social scientists to expand the frontiers of descriptive, predictive, and prescriptive analytics for understanding the dynamics and structure of social networks, adversarial bot or fake account creation and automation strategies, influence messaging detection, community detection, population characterization, knowledge graph construction, information diffusion analysis, content-driven and context-driven cyber forensics analysis for fake content and account detection (bot and deep fake detection), stance and co-occurrence network analysis, to name a few. Some of the main areas of focus include 1) Analytics for Influence Campaign Awareness and Sensemaking; 2) Analytics for Countering Micro-targeting Campaigns and Scams on Social Networks; 3) Analytics for Countering Fraudulent Accounts, Contents, and Activities on Social Networks.

These exemplary directions of studies show that social networks cybersecurity analytics is an emerging area of national concern and significance. This emphasizes the need for data scientists and engineers in both academic and industry who are experts in different domains of social networks cybersecurity analytics and can materialize the broad impact of these areas on all aspects of our life toward assuring national security and protecting businesses and individuals.