Chinese Journal of Network and Information Security ›› 2023, Vol. 9 ›› Issue (2): 21-32.doi: 10.11959/j.issn.2096-109x.2023018
• Comprehensive Reviews • Previous Articles Next Articles
Zhao CAI, Tao JING, Shuang REN
Revised:
2023-02-19
Online:
2023-04-25
Published:
2023-04-01
Supported by:
CLC Number:
Zhao CAI, Tao JING, Shuang REN. Survey on Ethereum phishing detection technology[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 21-32.
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检测方法 | 典型代表 | 原理 | 优点 | 缺点 |
基于 XGBoost 的以太坊异常账户检测方法[ | 从 EtherScamDB 和以太坊客户端上获取数据集,通过收集的交易信息提取了 42 个账户特征,使用XGBoost分类器进行分类,在训练集上训练模型,在测试集上预测结果,并采用十折交叉验证来评估模型的性能 | ● 提取特征较为充分,从交易历史记录中提取了42个特征 | ● 只考虑了节点的账户信息,忽略了节点的网络信息 | |
● 使用 XGBoost 检测精度高,平均准确率达到96.3%,平均AUC达到99.4% | ● 只考虑了交易成功的记录,结果具有一定的局限性 | |||
● 进行了特征重要性分析,评价了不同的特征对检测结果的影响程度 | ||||
基于交易信息的以太坊钓鱼检测方法 | 基于LightGBM的以太坊恶意账户检测方法[ | 收集交易记录进行特征构造,一部分是基于交易历史归纳总结的手工特征,一部分是使用自动特征构造工具 featuretools 提取的统计特征,最后采用6种监督机器学习方法来检测恶意账户 | ● 不仅考虑手工特征,还使用自动特征构造工具来提取特征 | ● 忽略交易网络的结构特征 |
● 检测结果较好,提出方法的 F1值达到94.9% | ● 不能解决数据不平衡问题 | |||
级联特征提取和双采样集成方法[ | 将以太坊交易历史记录建模成交易网络,考虑交易时间和交易金额,提取节点的n阶邻居信息,采用LightGBM作为基准模型进行双采样集成训练 | ● 解决了数据不平衡问题 | ● 忽略交易网络的结构特征 | |
● 考虑了交易的网络信息 | ● 没有考虑以太坊交易网络的动态特性 | |||
● 具有可扩展性 | ||||
Tran2Vec[ | 将交易网络建模成交易图,节点代表账户,边代表交易,采用改进后的有偏游走方式刻画图的结构特征,获得节点的低维向量表示,最后采用单类 SVM 进行检测 | ● 改进传统游走方式,更能反映以太坊交易网络特点 | ● 忽略以太坊交易网络多重图特性 | |
● 解决数据不平衡的问题 | ● 两阶段式方法具有局限性 | |||
基于图嵌入的以太坊钓鱼检测方法 | 基于时间加权多重图的检测方法[ | 将以太坊交易网络建模成时间加权多重有向图,每个节点之间可以存在多条边,且边具有交易金额和交易时间戳等权重信息 | ● 时间加权多重图更符合以太坊交易网络 | ● 两阶段式方法具有局限性 |
● 建模后的网络巨大,不适用于大型图 | ||||
构造交易子图[ | 根据目标账户构建交易子图,每个子图包含标签和账户周围的交易网络,用子图反映账户信息获得低维嵌入向量 | ● 大大缩小网络规模,容易处理大型图 | ● 两阶段式方法具有局限性 | |
● 图级表示检测效果较好 | ● 前期交易子图构造过程复杂 | |||
基于图神经网络的以太坊钓鱼检测方法 | 多通道图分类模型[ | 将高复杂度的节点分类任务转化为低复杂度的图分类任务,使用不同池化层提取不同层次的结构信息,最后再聚合池化图信息 | ● 复杂度低 | ● 依赖数据集的质量 |
● 模型精度高 | ● 检测时间较长 | |||
基于Chebyshev-GCN的以太坊钓鱼检测方法[ | 构建了轻量级以太坊交易网络,选取最大的弱连通子图,动态调整子图的大小 | ● 实现了端到端的检测模型 | ● 子图采样规则较复杂 | |
● 适合大型图 |
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