The FACE(TM) Standard is an Open Architecture approach to creating reusable, interoperable software for DoD Platforms (https://www.opengroup.org/face). The FACE Consortium was formed in 2010 to define an open avionics environment for all military airborne platform types. Vanderbilt has been an Academia team member supporting the FACE standard development effort since its inception in 2009 assisting in the formative concepts and continues to provide key technical guidance in many areas of the FACE Con

The Future Airborne Capability Environment (FACETM) Technical Standard (TS) defines a software Common Operating Environment (COE) that requires specific software design documentation, software architecture and integration analysis for each platform that is planning to adopt the FACETM Technical Standard to meet their Open Architecture (OA) requirement.

One of the most significant challenges in cybersecurity is that humans are involved in software engineering and inevitably make security mistakes in their implementation of specifications, leading to software vulnerabilities. A challenge to eliminating these mistakes is the relative lack of empirical evidence regarding what secure coding practices (e.g., secure defaults, validating client data, etc.), threat modeling, and educational solutions are effective in reducing the number of application-level vulnerabilities that software engineers produce.

Cyber-physical systems (CPS), such as automobiles, planes, and heavy equipment rely on complex distributed supply chains that source parts from manufacturers across the world. A fundamental problem that these systems face is ensuring the safety, security, and integrity of both the cyber components and physical parts that they receive through their supply chain.

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data.

Anomaly detection, prognostication and automated mitigation are very critical for data center management. Most of these approaches can be divided into two categories - model-based and data-driven. While model-based techniques rely on physics guided models that can explain and predict the expected progression of parameters such as temperature and voltage in electronics, the data-driven approach is suitable for complex scenarios where a suitable physics based model is unavailable. The data-driven approaches can be further divided into supervised and unsupervised methods.

Cyber-Physical Systems (CPS) are composed of a wide range of networked physical, computational, and human/organization components. These systems are highly complex as they have many different heterogeneous components, such as physical, computational, and human. Simulation-based evaluation of the behavior of CPS is complex, as it involves multiple, heterogeneous, interacting domains. Each simulation domain has sophisticated tools, but their integration into a coherent framework is a difficult, time-consuming, labor-intensive, and error-prone task.

Evaluating the cybersecurity of a complex Industrial Control Systems (ICS), such as the Railway Transportation System (RTS), against a variety of cyber threats is a significantly hard and multi-faceted problem.

Makerspaces are very popular because they provide a hands-on experience for young learners to experiment with technology. One drawback is that the focus of educational experiences in makerspaces are necessarily on the hardware. Computing aspects, especially more advanced concepts such as cybersecurity, take a back seat. We will team up with Martin Luther King Jr.