Author Login IJIRT Partner Network (IPN) Login

Airdrop Hunter Bot Sybil Identification Techniques

  • Unique Paper ID: 162025
  • Volume: 10
  • Issue: 7
  • PageNo: 244-252
  • 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.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 7
  • PageNo: 244-252

Airdrop Hunter Bot Sybil Identification Techniques

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry