Excellence in Research: Collaborative Research: Detecting Vulnerabilities in Internet of Things with Deep Learning

The Internet of Things (IoT) integrates software applications, physical devices, and algorithms to interact with the physical world and humans. The economic and societal potential of such systems is vastly greater than has been realized, and major investments are being made worldwide to develop the technology. The technology for building IoT is based on embedded systems, scientific computations, and software embedded in devices.

Because the physical components of IoT are directly interactive with humans, the security and reliability requirements are qualitatively different from those in general purpose computing. Failure to meet the security and reliability requirements exposes IoT and humans to malignant attacks. The goal of this project is to conduct interdisciplinary research that utilizes artificial intelligence methodologies against cybercriminals who initiate attacks or target internet connected devices and users.

This project aims to explore applications of Deep Learning in cybersecurity research to detect security vulnerabilities in the Internet of Things through automated digital forensic evidence analytics. The project will actively engage a team of researchers in the investigation of deep learning, which includes a broader family of Artificial Intelligence that has produced results comparable and in some cases superior to human experts, to conduct the following research activities: (1) Assessing potential data vulnerabilities related to personal data privacy violations by analyzing the extracted hidden contents evidence and encrypted messages from IoT devices in a forensically sound manner; (2) Evaluating IoT software forensic evidence.

Analyzing software vulnerabilities in IoT application source code to better mitigate the risk to software systems. Typical source code vulnerability evidence in applications includes buffer overflow, integer overflow, and Carriage Return and Line Feed injection; (3) Reconstructing attack scenes based on forensic evidence to find existing system vulnerabilities of IoT; (4) Increase research capacity and collaborations to generate new research opportunities for undergraduates from underrepresented communities to pursue advanced degrees in computer science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.