US-CERT study predicts machine learning, transport systems to become security risks

The Carnegie-Mellon University’s Software Engineering Institute has nominated transport systems, machine learning, and smart robots as needing better cyber-security risk and threat analysis.

That advice comes in the institute’s third Emerging Technology Domains Risk Survey, a project it has handled for the US Department of Homeland Security’s US-CERT since 2015. The surveys are cumulative, meaning any emerging technologies noted are in addition to those recommended for scrutiny in previous surveys. In other words, previously noted concerns are still live; it’s not like phishing and firewall security should be forgotten about just because the latest study focuses on AI and transport stuff.

The report “helps US-CERT identify vulnerabilities, promote good security practices, and understand vulnerability risk,” we’re told.

The institute’s CERT Coordination Centre (CERT/CC) sees machine learning as a potential security quagmire, since it expects aggressive adoption in the medium term, but use-cases are legion, making it difficult to observe from a security point of view. In its survey, published this month, the team stated:

“Characteristics of interest likely include big data applications dealing with sensitive information, security products whose efficacy depends on effective anomaly detection, and learning sensors that inform actions in physical reality (such as in self-driving vehicles).”

In its assessment of transport, the survey worries about long-term interconnectedness.

“Future intelligent transport systems will provide communications and data between connected and autonomous cars and trucks, road infrastructure, other types of vehicles, and even pedestrians and bicyclists,” the report notes.

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Source: The Register