Cybersecurity operators have increasingly relied on machine learning to address a rising number of threats. But will machine learning give them a decisive advantage or just help them keep pace with attackers? This report explores the history of machine learning in cybersecurity and the potential it has for transforming cyber defense in the near future.
Six Chinese universities have relationships with Advanced Persistent Threat (APT) hacking teams. Their activities range from recruitment to running cyber operations. These partnerships, themselves a case study in military-civil fusion, allow state-sponsored hackers to quickly move research from the lab to the field. This report examines these universities’ relationships with known APTs and analyzes the schools’ AI/ML research that may translate to future operational capabilities.
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.
Geographical, the magazine of the Royal Geographical Society, reviews CSET Senior Faculty Fellow Ben Buchanan's latest book, which highlights the landscape of subtle but persistent cyber attacks that are changing statecraft.
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.
Accelerating threats to cybersecurity, the impact of automation on cyber defense, and the degree to which cyber operations will become faster and more powerful are among the subjects that CSET will now start to explore thanks to a grant from the William and Flora Hewlett Foundation.
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