Cybersecurity

Automating Cyber Attacks

Ben Buchanan John Bansemer Dakota Cary Jack Lucas Micah Musser
| November 2020

Based on an in-depth analysis of artificial intelligence and machine learning systems, the authors consider the future of applying such systems to cyber attacks, and what strategies attackers are likely or less likely to use. As nuanced, complex, and overhyped as machine learning is, they argue, it remains too important to ignore.

U.S. Demand for Talent at the Intersection of AI and Cybersecurity

Cindy Martinez Micah Musser
| November 2020

As demand for cybersecurity experts in the United States has grown faster than the supply of qualified workers, some organizations have turned to artificial intelligence to bolster their overwhelmed cyber teams. Organizations may opt for distinct teams that specialize exclusively in AI or cybersecurity, but there is a benefit to having employees with overlapping experience in both domains. This data brief analyzes hiring demand for individuals with a combination of AI and cybersecurity skills.

The Future of Data Science

National Academies of Sciences, Engineering, and Medicine
| November 4, 2020

CSET Founding Director Jason Matheny presented the keynote address at the virtual colloquium on the future of data science and the implications for privacy and national security hosted by the National Academies of Sciences, Engineering, and Medicine.

Destructive Cyber Operations and Machine Learning

Dakota Cary Daniel Cebul
| November 2020

Machine learning may provide cyber attackers with the means to execute more effective and more destructive attacks against industrial control systems. As new ML tools are developed, CSET discusses the ways in which attackers may deploy these tools and the most effective avenues for industrial system defenders to respond.

"Deepfakes: A Grounded Threat Assessment" by Tim Hwang was cited in VentureBeat. Read the mention of CSET's research below.

One sentence summarizes the complexities of modern artificial intelligence: Machine learning systems use computing power to execute algorithms that learn from data. This AI triad of computing power, algorithms, and data offers a framework for decision-making in national security policy.

Machine learning advances are transforming cyber strategy and operations. This necessitates studying national security issues at the intersection of AI and cybersecurity, including offensive and defensive cyber operations, the cybersecurity of AI systems, and the effect of new technologies on global stability.