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A Sequence Learning Approach to Detecting Vulnerabilities

Jupyter Notebook 91.54% Python 8.46%

safe-smartcontracts's Introduction

Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Security Threats arXiv Paper

Folder---input_data

This folder contains 3 csv files of distinct suicidal, prodigal, and greedy FPs.

* clean_test_suicidalFP_noDups
* clean_test_leakFP_noDups
* clean_test_greedyFP_noDups

Folder---saved_model

This folder contains trained models, with the latest one being 'V6_620k_batch9'

* LSTM
* improved_LSTM
* V6_620k_batch9

Jupyter Notebooks

Jupyter notebooks for prototyping and evaluation:

* Learning Smart Contract Security Exploits
* Learning Smart Contract Security Exploits_addedFPs
* Learning Smart Contract Security Exploits_addedFPs_Evaluation

Python Scripts

Python scripts for running experiments:

* Learning Smart Contract Security Exploits
* Learning Smart Contract Security Exploits_addedFPs
* Learning Smart Contract Security Exploits_revised - the latest script which trains the LSTM model on code vectors of Smart Contracts.

Input Data

Link below:

https://drive.google.com/drive/folders/1KuKxknJ-uOWZOaymP9b3eOXa1hFNZxVS?usp=sharing

-Code for academic research.

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