Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2025-03-07 16:25
Performance Comparison of Machine Learning Models for Output Prediction Attacks and Their Interpretability
Hayato Watanabe (Tokai Univ/NICT), Ryoma Ito (NICT), Toshihiro Ohigashi (Tokai Univ/NICT) ICSS2024-121
Abstract (in Japanese) (See Japanese page) 
(in English) Watanabe et al. applied neural network (NN)-based output prediction attacks using LSTM, proposed by Kimura et al., to SIMON variants and demonstrated its effectiveness in identifying vulnerable structures of SIMON variants.
Such vulnerable structures were identified by the fact that the NN-based output prediction attack outperforms differential/linear distinguishing attacks in terms of the maximum number of attackable rounds.
Moreover, Watanabe et al. presented effective linear approximations in these vulnerable structures and suggested that the NN may capture them to facilitate output prediction attacks.
This study aims to explore whether LSTM is the optimal model for output prediction attacks and whether machine learning (ML) models can effectively capture the linear approximations presented by Watanabe et al.
Specifically, we compare the performance of six different ML models (four NN models and two decision tree models) for output prediction attacks and demonstrate that decision tree models outperform NN models.
Additionally, we perform SHAP analysis on decision tree models to visualize the basis of their predictions.
The analysis not only proves that the models capture the linear approximation but also suggests that they precisely identify all input bits that are strongly correlated with the target output bits.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Learning / Neural Network / Decision Tree / SIMON / SHAP / Visualize / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 422, ICSS2024-121, pp. 407-414, March 2025.
Paper # ICSS2024-121 
Date of Issue 2025-02-27 (ICSS) 
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 ICSS2024-121

Conference Information
Committee ICSS IPSJ-SPT  
Conference Date 2025-03-06 - 2025-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Prefectural Museum & Art Museum 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Security, Trust, etc. 
Paper Information
Registration To ICSS 
Conference Code 2025-03-ICSS-SPT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance Comparison of Machine Learning Models for Output Prediction Attacks and Their Interpretability 
Sub Title (in English)  
Keyword(1) Machine Learning  
Keyword(2) Neural Network  
Keyword(3) Decision Tree  
Keyword(4) SIMON  
Keyword(5) SHAP  
Keyword(6) Visualize  
Keyword(7)  
Keyword(8)  
1st Author's Name Hayato Watanabe  
1st Author's Affiliation Tokai University/NICT (Tokai Univ/NICT)
2nd Author's Name Ryoma Ito  
2nd Author's Affiliation NICT (NICT)
3rd Author's Name Toshihiro Ohigashi  
3rd Author's Affiliation Tokai University/NICT (Tokai Univ/NICT)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2025-03-07 16:25:00 
Presentation Time 20 minutes 
Registration for ICSS 
Paper # ICSS2024-121 
Volume (vol) vol.124 
Number (no) no.422 
Page pp.407-414 
#Pages 8 
Date of Issue 2025-02-27 (ICSS) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan