Research on Security Vulnerability Detection of Smart Contract

2022 ◽  
pp. 957-963
Author(s):  
Dongfang Jia ◽  
Zhicong Liu ◽  
Zhengqi Zhang ◽  
Jun Ye
Keyword(s):  
Vulnerability Detection ◽  
Security Vulnerability ◽  
Smart Contract

TokenCheck: Towards Deep Learning Based Security Vulnerability Detection In ERC-20 Tokens

2021 ◽  
Author(s):  
Subhasish Goswami ◽  
Rabijit Singh ◽  
Nayanjeet Saikia ◽  
Kaushik Kumar Bora ◽  
Utpal Sharma
Keyword(s):  
Deep Learning ◽  
Vulnerability Detection ◽  
Security Vulnerability

scholarly journals Open source software security vulnerability detection based on dynamic behavior features

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0221530
Author(s):  
Yuancheng Li ◽  
Longqiang Ma ◽  
Liang Shen ◽  
Junfeng Lv ◽  
Pan Zhang
Keyword(s):  
Open Source ◽  
Dynamic Behavior ◽  
Open Source Software ◽  
Software Security ◽  
Vulnerability Detection ◽  
Security Vulnerability

scholarly journals P-Fuzz: A Parallel Grey-Box Fuzzing Framework

2019 ◽  
Vol 9 (23) ◽  
pp. 5100
Author(s):  
Congxi Song ◽  
Xu Zhou ◽  
Qidi Yin ◽  
Xinglu He ◽  
Hangwei Zhang ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Distributed Computing ◽  
Software Testing ◽  
Vulnerability Detection ◽  
Security Vulnerability ◽  
Effective Technology ◽  
Speed Up ◽  
Data Races

Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59× and Roving about 1.66× on average by using 4 nodes.

DeeSCVHunter: A Deep Learning-Based Framework for Smart Contract Vulnerability Detection

2021 ◽  
Author(s):  
Xingxin Yu ◽  
Haoyue Zhao ◽  
Botao Hou ◽  
Zonghao Ying ◽  
Bin Wu
Keyword(s):  
Deep Learning ◽  
Vulnerability Detection ◽  
Smart Contract
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