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Machine Learning Powering IoT Security

Data security is critical for IoT to flourish as lack of consumer confidence can impede adoption. Machine Learning enables expedited threat detection, investigation, and remediation for safer and more robust IoT deployments.

Mark Micallef, Cloudera

The cybersecurity risk posed by IoT will increase dramatically as the number of IoT-connected devices proliferate worldwide, driving an ever-expanding attack surface that malicious actors will seek to exploit.

Jason Kichen, Versive

Today’s Iot Security risks are enormous, with new vulnerabilities always surfacing. Existing solutions tackle parts of the problem. Startups are using Machine Learning to cover the entire IoT security waterfront.

Rick Grinnell, Glasswing Ventures

The speed of adoption and devices will continue, and the awareness of the aspects of IoT to be wary of will drive the push for increased security built in to the devices.

Ron Schlecht, BTB Security

IoT devices are currently being developed inherently insecurely. With the number of devices exploding, IoT will morph towards self detection and remediation underpinned by machine learning capability.

Ashwin Pal, Unisys

8 out of 10 IoT devices are insecure – we’ll start seeing anomaly based detections start to pick up abnormal behaviour in networks and systems.

Andrew Constantine, CIO Cyber Security

This post first appeared on Flarrio - Creating Competitive Edge Using Emerging, please read the originial post: here

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Machine Learning Powering IoT Security


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