Intelligent Transit Systems Tops 2017 US-CERT Risk Concerns

In its annual emerging technology domains risk survey, the Department of Homeland Security’s Computer Emergency Readiness Team (CERT) and the Software Engineering Institute at Carnegie Mellon University identified intelligent transit systems as their top domain for future high-priority analysis. The report also identifies machine learning and smart robotics as related dependencies and areas of interest as well.

The CERT Coordination Center (CERT/CC) researched the emerging technology trends through 2025 to assess the technology domains that will become successful and transformative, as well as the potential cybersecurity impact of each domain. The annual report provides an understanding of cybersecurity issues that may result as part of each domain’s adoption in the future. Intelligent transit systems, machine learning, and smart robotics emerged as the most interesting domains. The report states that “unexpected interactions between hardware and software subcomponents can magnify the impact of a vulnerability.”

Other domains in the survey were viewed as less widely applicable in the near future. For example, blockchain and virtual personal assistants, the report said, may appeal only to early adopters. Others, such as smart buildings or robotic surgery, are specific to one industry. “The CERT/CC does not expect these domains to be adopted as widely in the next 12–18 months,” the report concluded.

Intelligent transportation systems will affect virtually everyone. World economies depend on efficient and functioning cars, buses, mass transit, boats, and planes. Additionally, intelligent transit systems will control fast-moving and large vehicles, which will impact individual safety.

For autonomous transit systems to make better decisions on the fly, machine learning and robotics must also emerge as dependent domains requiring further study.

Autonomous cars, the report argues, is a type of robot. Informing the autonomous car will be a large database of machine learning, based on road maps, road conditions, weather conditions, and other variables. How these domains interact and work in the real world remains to be seen.

Machine learning goes beyond automotive. It has practical application in finance, public policy, and engineering. Certainly, the military has use for machine learning as do other complex industries.

Smart robotics will influence future construction projects, logistics, manufacturing, and other subtler industries, such as hospitality where robotic butlers are being used to deliver towels and food items to guests.

Curiously, one limiting criteria in the report appears to be whether or not there are existing vulnerabilities for these technology domains. For example, the report notes that blockchain has already endured multiple vulnerabilities already. However, CERT/CC states it is not aware of any exploitations of self-driving cars or traffic lights that are being tested or deployed in the public.