Particle Swarm Algorithm for Smart Contract Vulnerability Detection Based on Semantic Web

Author:

Feng Tao1,Cui Yuyang1

Affiliation:

1. School of Computer and Communication, Lanzhou University of Technology, China

Abstract

In recent years, smart contracts have risen rapidly in the blockchain field, but security issues have also become increasingly prominent. Due to the lack of unified evaluation standards, the security analysis of smart contracts mainly relies on complex and not easily scalable expert rules. To address these issues, we employ slicing techniques to reduce the interference of extraneous code on the detection process, apply normalisation techniques to eliminate the differences between different compiler versions and use particle swarm optimisation algorithms to determine the similarity between contracts, thus improving the accuracy and efficiency of detection. In addition, we combine a variety of features such as static analysis, dynamic analysis and symbolic execution to gain a more comprehensive understanding of contract characteristics and behaviours for more accurate vulnerability identification. Experimental results show that the scheme significantly improves the detection capability and provides a new solution for the security detection of smart contracts.

Publisher

IGI Global

Reference41 articles.

1. The monarch butterfly optimization algorithm for solving feature selection problems.;M.Alweshah;Neural Computing & Applications,2020

2. Optimization of the Wake-Up Scheduling Using a Hybrid of Memetic and Tabu Search Algorithms for 3D-Wireless Sensor Networks

3. Understanding Code Reuse in Smart Contracts

4. Datar, M., Altman, E., De Pellegrini, F., El Azouzi, R., & Touati, C. (2020). A mechanism for price differentiation and slicing in wireless networks. In Proceedings of the 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT) (pp. 1–8). IEEE.

5. Asm2 Vec: Boosting static representation robustness for binary clone search against code obfuscation and compiler optimization.;S. H. H.Ding;Proceedings for the IEEE Symposium on Security and Privacy SP,2019

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3