Artificial intelligence (AI) and machine learning (ML) are increasingly being used in cyber
operations. Because of techniques like adversarial learning, the performance of network …

There is a growing orientation of cyber systems, technologies, and processes away from
notions of cybersecurity and towards notions of cyber resilience. In multi-domain operation …

This paper addresses the urgent need for messaging standards in the operational test and
evaluation (T&E) of machine learning (ML) applications, particularly in edge ML applications …

Wide-spread use of transfer learning establishes inter-linkages between otherwise disparate
parts of systems. These inter-linkages create systemic risks of cascading failure. This paper …

There is an increasing demand for operational uses of machine learning (ML), however, a
lack of best practices for test and evaluation (T &E) of learning systems is a hindrance to …

Multi-domain operations (MDO) are characterized by simultaneous and sequential
operations; rapid and continuous integration; and surprise. Machine learning (ML) for MDO …

The endogenous evolution of intelligent systems exposes insufficiencies in some systems
engineering activities that rely on exogenous properties of systems. One of these activities is …

According to security. org, as of 2023, 65% of credit card (CC) users in the US have been
subjected to fraud at some point in their lives, which equates to about 151 million Americans …

While machine learning models have demonstrated the ability to detect cyber attacks, their
deployment to operational scenarios is often limited due to the possibility of the model failing …

Recently, combinatorial interaction testing (CIT) has been applied to machine learning.
Recent results demonstrate that combinatorial coverage metrics can correlate with …