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Paper Abstract and Keywords
Presentation 2026-06-12 10:35
Detection and prevention methods for demand-triggered Sybil attacks in IPFS
Takakazu Himeo, Noriaki Kamiyama (Ritsumeikan Univ) NLP2026-17 CCS2026-17
Abstract (in Japanese) (See Japanese page) 
(in English) The current Web uses a location-based mechanism for accessing information.
This method concentrates a great deal of responsibility and authority in the hands of
the administrators of the servers that provide the information, allowing them to
delete, alter, or censor information at their discretion.
As a result, information on the Internet is dominated by a few large platforms.
The InterPlanetary File System (IPFS) was developed to fundamentally address these
issues of centralized management.
IPFS operates on a P2P network and employs a decentralized mechanism in which
each node autonomously stores and shares information.
This allows information to be managed without relying on centralized operation,
making it resistant to censorship and tampering and highly tolerant to failures.
However, the current IPFS protocol is vulnerable to Sybil attacks.
A Sybil attack disrupts the network by generating a large number of fake nodes.
When such an attack occurs, the attacking nodes prevent routing to legitimate nodes,
effectively enabling censorship.
Therefore, this research aims to establish an on-demand method for detecting Sybil
attacks in IPFS and to examine techniques for identifying attacker hosts.
The proposed method initiates measurements of nearby peers when a user requests
content, and analyzes the Peer ID distribution of the responding nodes in an XOR
metric space.
Under normal conditions, PIDs near a CID are roughly evenly distributed, whereas
under a Sybil attack, bias and a decrease in distributional entropy appear.
Based on this difference, deviation from the reference distribution is used as an
indicator to detect anomalies.
Furthermore, due to the nature of on-demand detection, the detection rate and cost
vary depending on the popularity of the content.
We evaluate these effects and propose an efficient and effective method for detecting
Sybil attacks.
Keyword (in Japanese) (See Japanese page) 
(in English) IPFS / Sybil attack / KL divergence / on-demand detection / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 69, CCS2026-17, pp. 87-92, June 2026.
Paper # CCS2026-17 
Date of Issue 2026-06-04 (NLP, CCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLP2026-17 CCS2026-17

Conference Information
Committee CCS NLP  
Conference Date 2026-06-11 - 2026-06-12 
Place (in Japanese) (See Japanese page) 
Place (in English) I-site Namba 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, Complex Communication Sciences, etc. 
Paper Information
Registration To CCS 
Conference Code 2026-06-CCS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Detection and prevention methods for demand-triggered Sybil attacks in IPFS 
Sub Title (in English)  
Keyword(1) IPFS  
Keyword(2) Sybil attack  
Keyword(3) KL divergence  
Keyword(4) on-demand detection  
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1st Author's Name Takakazu Himeo  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ)
2nd Author's Name Noriaki Kamiyama  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ)
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Speaker Author-1 
Date Time 2026-06-12 10:35:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # NLP2026-17, CCS2026-17 
Volume (vol) vol.126 
Number (no) no.68(NLP), no.69(CCS) 
Page pp.87-92 
#Pages 6 
Date of Issue 2026-06-04 (NLP, CCS) 

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