DiAne is a smart fuzzer for IoT devices

Related tags

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
Reimplementation of Learning Mesh-based Simulation With Graph Networks

Pytorch Implementation of Learning Mesh-based Simulation With Graph Networks This is the unofficial implementation of the approach described in the pa

Jingwei Xu 33 Dec 14, 2022
Materials for my scikit-learn tutorial

Scikit-learn Tutorial Jake VanderPlas email: [email protected] twitter: @jakevdp gith

Jake Vanderplas 1.6k Dec 30, 2022
Localized representation learning from Vision and Text (LoVT)

Localized Vision-Text Pre-Training Contrastive learning has proven effective for pre- training image models on unlabeled data and achieved great resul

Philip Müller 10 Dec 07, 2022
[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

SMYRF: Efficient attention using asymmetric clustering Get started: Abstract We propose a novel type of balanced clustering algorithm to approximate a

Giannis Daras 46 Dec 22, 2022
The fastai deep learning library

Welcome to fastai fastai simplifies training fast and accurate neural nets using modern best practices Important: This documentation covers fastai v2,

fast.ai 23.2k Jan 07, 2023
Repository for GNSS-based position estimation using a Deep Neural Network

Code repository accompanying our work on 'Improving GNSS Positioning using Neural Network-based Corrections'. In this paper, we present a Deep Neural

32 Dec 13, 2022
Efficient Training of Visual Transformers with Small Datasets

Official codes for "Efficient Training of Visual Transformers with Small Datasets", NerIPS 2021.

Yahui Liu 112 Dec 25, 2022
This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Gait3D-Benchmark This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild

82 Jan 04, 2023
Modular Gaussian Processes

Modular Gaussian Processes for Transfer Learning 🧩 Introduction This repository contains the implementation of our paper Modular Gaussian Processes f

Pablo Moreno-Muñoz 10 Mar 15, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

Vikash Sehwag 65 Dec 19, 2022
[3DV 2021] Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation

Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation This is the official implementation for the method described in Ch

Jiaxing Yan 27 Dec 30, 2022
CS50x-AI - Artificial Intelligence with Python from Harvard University

CS50x-AI Artificial Intelligence with Python from Harvard University 📖 Table of

Hosein Damavandi 6 Aug 22, 2022
Reinforcement Learning for Automated Trading

Reinforcement Learning for Automated Trading This thesis has been realized for the obtention of the Master's in Mathematical Engineering at the Polite

Pierpaolo Necchi 80 Jun 19, 2022
KIDA: Knowledge Inheritance in Data Aggregation

KIDA: Knowledge Inheritance in Data Aggregation This project releases our 1st place solution on NeurIPS2021 ML4CO Dual Task. Slide and model weights a

24 Sep 08, 2022
A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017

Semantic Image Synthesis via Adversarial Learning This is a PyTorch implementation of the paper Semantic Image Synthesis via Adversarial Learning. Req

Seonghyeon Nam 146 Nov 25, 2022
[ICCV' 21] "Unsupervised Point Cloud Pre-training via Occlusion Completion"

OcCo: Unsupervised Point Cloud Pre-training via Occlusion Completion This repository is the official implementation of paper: "Unsupervised Point Clou

Hanchen 204 Dec 24, 2022
Kaggle Ultrasound Nerve Segmentation competition [Keras]

Ultrasound nerve segmentation using Keras (1.0.7) Kaggle Ultrasound Nerve Segmentation competition [Keras] #Install (Ubuntu {14,16}, GPU) cuDNN requir

179 Dec 28, 2022
House3D: A Rich and Realistic 3D Environment

House3D: A Rich and Realistic 3D Environment Yi Wu, Yuxin Wu, Georgia Gkioxari and Yuandong Tian House3D is a virtual 3D environment which consists of

Meta Research 1.1k Dec 14, 2022
Official implementation of deep-multi-trajectory-based single object tracking (IEEE T-CSVT 2021).

DeepMTA_PyTorch Officical PyTorch Implementation of "Dynamic Attention-guided Multi-TrajectoryAnalysis for Single Object Tracking", Xiao Wang, Zhe Che

Xiao Wang(王逍) 7 Dec 03, 2022