DiAne is a smart fuzzer for IoT devices

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Deep Learningdiane
Overview

Diane

Diane is a fuzzer for IoT devices. Diane works by identifying fuzzing triggers in the IoT companion apps to produce valid yet under-constrained inputs. Our key observation is that there exist functions inside the companion apps that are executed before any data-transforming functions (e.g., network serialization), but after the input validation code.

Repository structure

Code and data will be released soon!

Research paper

We present our approach and the findings of this work in the following research paper:

DIANE: Identifying Fuzzing Triggers in Apps to Generate Under-constrained Inputs for IoT Devices [PDF]
Nilo Redini, Andrea Continella, Dipanjan Das, Giulio De Pasquale, Noah Spahn, Aravind Machiry, Antonio Bianchi, Christopher Kruegel, Giovanni Vigna.
In Proceedings of the IEEE Symposium on Security & Privacy (S&P), May 2021

If you use Diane in a scientific publication, we would appreciate citations using this Bibtex entry:

@inproceedings{redini_diane_21,
 author = {Nilo Redini and Andrea Continella and Dipanjan Das and Giulio De Pasquale and Noah Spahn and Aravind Machiry and Antonio Bianchi and Christopher Kruegel and Giovanni Vigna},
 booktitle = {In Proceedings of the IEEE Symposium on Security & Privacy (S&P)},
 month = {May},
 title = {{DIANE: Identifying Fuzzing Triggers in Apps to Generate Under-constrained Inputs for IoT Devices}},
 year = {2021}
}
Owner
seclab
The Computer Security Group at UC Santa Barbara
seclab
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