Paper Abstract and Keywords |
Presentation |
2019-07-24 09:30
Generation of Family Resemblance Inference Rules by Boid Annotation and Labeled-LDA
-- A Machine Learning Approach to Integrate Inference Attack Analysis and Covert Channel Attack Analysis -- Kosuke Kurebayashi, Tetsuya Morizumi, Hirotsugu Kinoshita (KU) ISEC2019-42 SITE2019-36 BioX2019-34 HWS2019-37 ICSS2019-40 EMM2019-45 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper we propose a method for machine learning similar chains of words (word chains) similar to "rules for inferring attack and covert channel" using artificial intelligence. The two machine learning elements of this method are Boid annotations (generate a word chain similar to a critical proposition involved in an inference attack with word2vec, and make the word chain a family-like group by the Boid algorithm), and Labeled-LDA (topic analysis of the text linked to the learned critical word chain). These elements are a combination of pointwise mutual information evaluation, KL information evaluation, and average mutual information evaluation repeatedly and repeatedly. That is, a scale on which the transition law is established, a scale on which the perfect law is established, and a scale on which the equivalence relation is established, meaning that the human interprets the text by evaluating the text random variables from different perspectives It is based on the assumption that it is possible to approximate context). In addition, the concept that penetrates objects learned by Boid annotation and L-LDA is based on the similarity relation that does not satisfy the equivalence relation of "Family Resemblance" in the text and word chain. Furthermore, Boid annotations and Labeled-LDA's multi-layered combination learning are used for machine learning of the teaching data itself and machine learning of the text to be evaluated. The critical word chain learned by the teacher text that describes the security model is used as the label of L-LDA for evaluation. By this calculation, a text corresponding to a critical word chain related to an inference attack and a covert channel attack is learned from a collection of texts. The learning result is referred to as an inference rule for covert channel and inference channel analysis learning of access control. In this paper, in the system based on this method, the concrete design of Boid annotation, that is, the syntax analysis of SQLite, word and text, and the concrete design of Word2vec and Boid algorithm using them are also mentioned. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Artificial intelligence / Machine learning / Access control / Probabilistic security model / Bayesian probabilistic model / LDA / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 141, SITE2019-36, pp. 243-249, July 2019. |
Paper # |
SITE2019-36 |
Date of Issue |
2019-07-16 (ISEC, SITE, BioX, HWS, ICSS, EMM) |
ISSN |
Print edition: ISSN 0913-5685 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) |
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ISEC2019-42 SITE2019-36 BioX2019-34 HWS2019-37 ICSS2019-40 EMM2019-45 |
Conference Information |
Committee |
ISEC SITE ICSS EMM HWS BioX IPSJ-CSEC IPSJ-SPT |
Conference Date |
2019-07-23 - 2019-07-24 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kochi University of Technology |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Security, etc. |
Paper Information |
Registration To |
SITE |
Conference Code |
2019-07-ISEC-SITE-ICSS-EMM-HWS-BioX-CSEC-SPT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Generation of Family Resemblance Inference Rules by Boid Annotation and Labeled-LDA |
Sub Title (in English) |
A Machine Learning Approach to Integrate Inference Attack Analysis and Covert Channel Attack Analysis |
Keyword(1) |
Artificial intelligence |
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Machine learning |
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Access control |
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Probabilistic security model |
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Bayesian probabilistic model |
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LDA |
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1st Author's Name |
Kosuke Kurebayashi |
1st Author's Affiliation |
Kanagawa University (KU) |
2nd Author's Name |
Tetsuya Morizumi |
2nd Author's Affiliation |
Kanagawa University (KU) |
3rd Author's Name |
Hirotsugu Kinoshita |
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Kanagawa University (KU) |
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Speaker |
Author-1 |
Date Time |
2019-07-24 09:30:00 |
Presentation Time |
25 minutes |
Registration for |
SITE |
Paper # |
ISEC2019-42, SITE2019-36, BioX2019-34, HWS2019-37, ICSS2019-40, EMM2019-45 |
Volume (vol) |
vol.119 |
Number (no) |
no.140(ISEC), no.141(SITE), no.142(BioX), no.143(HWS), no.144(ICSS), no.145(EMM) |
Page |
pp.243-249 |
#Pages |
7 |
Date of Issue |
2019-07-16 (ISEC, SITE, BioX, HWS, ICSS, EMM) |
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