Honeypot Method to Lure Attackers Without Holding Crypto-Assets

被引:0
|
作者
Uchibori, Hironori [1 ]
Yoshioka, Katsunari [2 ]
Omote, Kazumasa [3 ,4 ]
机构
[1] Univ Tsukuba, Grad Sch Sci & Technol, Tsukuba 3058573, Japan
[2] Yokohama Natl Univ, Grad Sch Environm & Informat Sci, Yokohama 2408501, Japan
[3] Univ Tsukuba, Fac Engn Informat & Syst, Tsukuba 3058573, Japan
[4] Natl Inst Informat & Commun Technol, Koganei 1848795, Japan
关键词
Blockchains; Bitcoin; Peer-to-peer computing; Servers; Decentralized applications; Phishing; Smart contracts; Blockchain; clustering; ethereum; honeypot; !text type='JSON']JSON[!/text]-RPC;
D O I
10.1109/ACCESS.2024.3357785
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, the convenience and potential use of crypto-assets such as Bitcoin and Ethereum have attracted increasing attention. On the other hand, there have been reports of attacks on the blockchain networks that support crypto-assets in an attempt to steal other users' assets. In the past, research on attack observation against blockchains has used techniques such as holding real crypto-assets to lure attackers into honeypots or falsifying balances to attackers. However, these methods risk losing crypto-assets to attackers or being exposed as honeypots to attackers. To solve these problems, we propose a new RPC (Remote Procedure Call) honeypot method that returns the wallet address of another party holding a high balance in response to an attacker's request, thereby luring the attacker without having the real crypto-assets. Our experimental evaluation shows that this method can attract more attackers than the method with zero-balance wallets and can observe more sophisticated attacks. Furthermore, we proposed a risk reduction strategy for crypto-asset theft by applying the idea of our method. In the log analysis process, we devised a new clustering method using the number of times an attacker executes a specific method as a feature. By applying this method, we successfully classified attackers based on their objectives, demonstrating the efficient analysis of vast amounts of log data.
引用
收藏
页码:16059 / 16071
页数:13
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