CSIAC collects and publishes articles related to our technical focus areas on the web to share with the DoD community.
Artificial intelligence (AI) and machine learning (ML) represent an increasingly exciting field of computer science. A term originally coined by John McCarthy in 1956,1 AI is becoming increasingly pervasive in today’s world.
FORT MEADE, Md. – The National Security Agency (NSA) joins the Federal Bureau of Investigation (FBI), the Cybersecurity and Infrastructure…
ALBUQUERQUE, N.M. — Sandia National Laboratories and Arizona State University, two research powerhouses, are collaborating to push the boundaries of…
U2opia Technology has licensed Situ and Heartbeat, a package of technologies from the Department of Energy’s Oak Ridge National Laboratory…
The director of the National Security Agency said the agency’s new Artificial Intelligence Security Center is paying dividends in the…
FORT GEORGE G. MEADE, Md.   –  Mr. Michael Clark, deputy director of plans and policy at U.S. Cyber Command, presented a…
FORT MEADE, Md. – The National Security Agency (NSA) is launching its annual Codebreaker Challenge, offering students from U.S.-based academic…
The U.S. Department of Defense announced the release of Version 4.2 of the Online Cyber Resilient Weapon Systems Body of…
The Cybersecurity & Infrastructure Security Agency (CISA) has released an analysis and infographic detailing the findings from the 143 risk…
With the growing integration of artificial intelligence (AI) in cybersecurity, this article investigates the economic principles of substitution and scale’s elasticity to evaluate their impact on the return on security investment. Recognizing the potential of AI technologies to substitute human labor and traditional cybersecurity mechanisms and the significance of cost ramifications of scaling AI solutions within cybersecurity frameworks, the study theoretically contributes to understanding the financial and operational dynamics of AI in cybersecurity. It provides valuable insights for cybersecurity practitioners in public and private sectors. Through this analysis, ways in which AI technologies can redefine economic outcomes in cybersecurity efforts are highlighted. Strategic recommendations are also offered for practitioners to optimize the economic efficiency and effectiveness of AI in cybersecurity, emphasizing a dynamic, nuanced approach to AI investment and deployment.
When performing defense system analysis with simulation models, a great deal of time and effort is expended creating representations of real-world scenarios in U.S. Department of Defense (DoD) simulation tools. However, once these models have been created and validated, analysts rarely retrieve all the knowledge and insights that the models may yield and are limited to simple explorations because they do not have the time and training to perform more complex analyses manually. Additionally, they do not have software integrated with their simulation tools to automate these analyses and retrieve all the knowledge and insights available from their models.
In the digital age, the cyber domain has become an intricate network of systems and interactions that underpin modern society. Sim2Real techniques, originally developed with notable success in domains such as robotics and autonomous driving, have gained recognition for their remarkable ability to bridge the gap between simulated environments and real-world applications. While their primary applications have thrived in these domains, their potential implications and applications within the broader cyber domain remain relatively unexplored. This article examines the emerging intersection of Sim2Real techniques and the cyber realm, exploring their challenges, potential applications, and significance in enhancing our understanding of this complex landscape.