Methods and Apparatus for Addressing IoT Security Issues


At least 20 billion devices will be connected to the internet by 2023, transmitting sensitive personal data in real-time on a continuous, year-round basis. Unfortunately, current cybersecurity technologies are failing to prevent massive malware attacks and effectively address other internet security issues. FAU’s researchers have taken a new approach to address the problems inherent in Internet of Things (IoT) cybersecurity by implementing a binary classifier based upon a convolution neural network in conjunction with active measurements. This approach allows them to create a “fingerprint” for either a healthy or compromised IoT device in the dark web, and aids in the identification of malicious traffic before a cyber attack can occur.

Patent Information:
Secure Systems
For Information, Contact:
Dana Vouglitois
Florida Atlantic University
Elias Bou-Harb
Morteza Safaei Pour
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