Airdrop Hunter Bot Sybil Identification Techniques
Author(s):
Ishan K Dharpawar, Nandini Dange , Varun Dhumal, Sakshi Patil
Keywords:
cryptocurrency, airdrop campaigns, token distribution, airdrop hunter bots, Sybil identification techniques, blockchain analysis, social network pattern recognition, machine learning algorithms, genuine participants, ethical considerations, user privacy, research and development, fairness, integrity, security measures, wallet activity, patterns of interaction, anomalies, manipulation, cryptocurrency projects, secure ecosystem
Abstract
In the realm of cryptocurrency airdrop campaigns, concerns have been raised regarding the fair distribution of tokens due to the increasing prevalence of airdrop hunter bots. A range of Sybil identification techniques has been investigated in this research paper, including blockchain analysis, social network pattern recognition, and machine learning algorithms. The effectiveness of these methodologies in distinguishing genuine participants from Sybil actors is assessed, and ethical considerations related to user privacy are taken into account. The importance of continuous research and development in the field of Sybil identification is emphasized, as it is crucial to safeguard the fairness and integrity of token distributions within cryptocurrency communities, mitigating the disruptive influence of airdrop hunter bots and fostering a more secure and equitable ecosystem.
Article Details
Unique Paper ID: 162025

Publication Volume & Issue: Volume 10, Issue 7

Page(s): 244 - 252
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