A Feature-Based Robust Method for Abnormal Contracts Detection in Ethereum Blockchain

计算机科学 过度拟合 智能合约 块链 数据库事务 分类器(UML) 人工智能 机器学习 Boosting(机器学习) 操作码 数据挖掘 梯度升压 多数决原则 随机森林 计算机安全 数据库 人工神经网络 计算机硬件
作者
Ali Aljofey,Abdur Rasool,Qingshan Jiang,Qiang Qu
出处
期刊:Electronics [MDPI AG]
卷期号:11 (18): 2937-2937 被引量:17
标识
DOI:10.3390/electronics11182937
摘要

Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of this, previous machine learning methods have suffered from a number of disadvantages: first, it is extremely difficult to identify features that enable accurate detection of abnormal contracts, and based on these features, statistical analysis is also ineffective. Second, they ignore the imbalances and repeatability of smart contract accounts, which often results in overfitting of the model. In this paper, we propose a data-driven robust method for detecting abnormal contract accounts over the Ethereum Blockchain. This method comprises hybrid features set by integrating opcode n-grams, transaction features, and term frequency-inverse document frequency source code features to train an ensemble classifier. The extra-trees and gradient boosting algorithms based on weighted soft voting are used to create an ensemble classifier that balances the weaknesses of individual classifiers in a given dataset. The abnormal and normal contract data are collected by analyzing the open source etherscan.io, and the problem of the imbalanced dataset is solved by performing the adaptive synthetic sampling. The empirical results demonstrate that the proposed individual feature sets are useful for detecting abnormal contract accounts. Meanwhile, combining all the features enhances the detection of abnormal contracts with significant accuracy. The experimental and comparative results show that the proposed method can distinguish abnormal contract accounts for the data-driven security of blockchain Ethereum with satisfactory performance metrics.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
祈雨的鲸鱼完成签到 ,获得积分10
1秒前
狂野萤应助LX采纳,获得20
1秒前
归海含烟完成签到,获得积分10
2秒前
3秒前
王小美完成签到,获得积分10
4秒前
5秒前
爆米花应助x111采纳,获得10
5秒前
5秒前
鱼儿发布了新的文献求助10
8秒前
10秒前
嗯哼应助北极星采纳,获得10
10秒前
10秒前
汉堡包应助美好的隶采纳,获得10
11秒前
lalala应助Forever采纳,获得10
14秒前
zxx完成签到,获得积分10
16秒前
malizewski发布了新的文献求助10
16秒前
艾森豪威尔完成签到 ,获得积分10
16秒前
18秒前
雨雪霏霏啊完成签到,获得积分10
18秒前
maox1aoxin应助等风来采纳,获得60
18秒前
18秒前
自然秋双完成签到,获得积分10
19秒前
lx完成签到,获得积分10
20秒前
20秒前
hh完成签到 ,获得积分10
21秒前
游标卡尺完成签到,获得积分10
21秒前
NPC-CBI发布了新的文献求助10
22秒前
魔幻的雁完成签到,获得积分10
22秒前
D&L完成签到,获得积分10
23秒前
shunlibiye完成签到,获得积分10
23秒前
游标卡尺发布了新的文献求助10
25秒前
26秒前
naldo关注了科研通微信公众号
27秒前
malizewski完成签到,获得积分10
28秒前
英姑应助白芷苏采纳,获得20
29秒前
丰富秋烟发布了新的文献求助10
29秒前
30秒前
30秒前
lalala应助Forever采纳,获得10
31秒前
江沉晚吟完成签到 ,获得积分10
32秒前
高分求助中
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Very-high-order BVD Schemes Using β-variable THINC Method 870
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 800
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 800
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3256496
求助须知:如何正确求助?哪些是违规求助? 2898727
关于积分的说明 8301929
捐赠科研通 2567817
什么是DOI,文献DOI怎么找? 1394748
科研通“疑难数据库(出版商)”最低求助积分说明 652913
邀请新用户注册赠送积分活动 630602