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Affiliation |
IWATE University Faculty of Education Technology Education |
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Position |
Professor |
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Year of Birth |
1955 |
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Laboratory Address |
〒020-8550 3-18-33, Ueda, Morioka |
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Laboratory Phone number |
+81-19-6216534 |
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Laboratory Fax number |
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Mail Address |
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YOSHIDA Hitoaki
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Research Interests 【 display / non-display 】
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Information Science
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Complex System
Graduating School 【 display / non-display 】
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-1980.03
Tohoku University Faculty of Science Department of Chemistry Graduated
Graduate School 【 display / non-display 】
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-1987.03
Tohoku University Graduate School, Division of Natural Science Chemistry Doctor's Course Completed
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-1984.03
Tohoku University Graduate School, Division of Natural Science Chemistry Master's Course Completed
Degree 【 display / non-display 】
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Tohoku University - Doctor of Science 1987.01.01
Campus Career 【 display / non-display 】
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2014.04-Now
IWATE University Faculty of Education Technology Education Professor [Duty]
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2007.04-2014.03
IWATE University Center for Information and Media Associate Professor [Duty]
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2002.04-2007.03
IWATE University Associate Professor (As Old Post Name) [Duty]
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1997.04-2002.03
IWATE University Associate Professor (As Old Post Name) [Duty]
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1991.12-1996.03
IWATE University Research Assistant [Duty]
External Career 【 display / non-display 】
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1989.04-1991.11
University of Tsukuba Research Assistant
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1987.04-1989.03
University of Tsukuba Tsukuba University, technical official
Research Areas 【 display / non-display 】
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Informatics / Intelligent informatics
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Informatics / Computer system
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Informatics / Computational science
Course Subject 【 display / non-display 】
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2002
Advanced Simulation Engineering
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2002
Basic Computer Science
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2002
Seminar on English Presentation
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2003
Advanced Simulation Engineering
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2003
Basic Computer Science
Research Career 【 display / non-display 】
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Complex Syetem
Periods of research：
9999.01Keywords : Complex Syetem
Style of Research： Collaboration in Japan
Research Program： (not selected)
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Cryptosystem on Chaos Neural Network
Periods of research：
9999.01Keywords : cryptosystem,chaos,neural network
Style of Research： Collaboration in Japan
Research Program： (not selected)
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Construction of e-Regional Community
Periods of research：
9999.01Keywords : e-regional community
Style of Research： Collaboration in Japan
Research Program： (not selected)
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Chaos Neural Network
Periods of research：
9999.01Keywords : chaos,neural network,artificial neuron
Style of Research： Collaboration in Japan
Research Program： (not selected)
Published Papers 【 display / non-display 】
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Deep neural network simulates tempos estimated by one woodwind instruments from sheet music
Satoshi Kawamura, Zhongda Liu, Takeshi Murakami, Kenichi Watanabe, Masanori Hasewaga, Katsushi Ushiwata, Jun'ichi Shirafuji and Hitoaki Yoshida
1 1 - 20 2022.09 [Refereed]
Academic Journal Multiple authorship
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Chaotic and random neural network cipher for in-vehicle network security
Zhongda Liu, Takeshi Murakami, Satoshi Kawamura, and Hitoaki Yoshida
IEICE Communications Express 10 ( 12 ) 1026 - 1031 2022.01 [Refereed]
Academic Journal Multiple authorship
With the proliferation of automotive electronic devices, electronic control units are increasingly used to share information within in-vehicle networks. This induces a range of security risks, such as information disclosure, spoofing, and tampering. In this paper, we propose a symmetric-key cipher. The method generates pseudo-random numbers using a chaotic and random neural network, and encrypts and decrypts frame messages of in-vehicle networks based on the symmetric key. We also propose a lightweight ID-based key sharing protocol. We evaluated the key sharing, encryption, and decryption in Controller Area Network.
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Non-Attractive Periodic Trajectory Formation Mechanism on Random and Chaotic Time Series
Hitoaki YOSHIDA and Takeshi MURAKAMI
Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques ( IOS Press ) 327 197 - 208 2020.09
Academic Journal Multiple authorship
Pseudo-random number series extracted from chaotic and random time series from the chaotic and random neural network (CRNN) with fixed-point arithmetic has been the focus of attention for protecting the information security of IoT devices. Pseudo-random number series generated by a computer is eventually periodic, practically. The produced closed trajectory is not a limit cycle, because which does not divide the phase space into 2 regions. The closed trajectory in this work is called a non-attractive periodic trajectory (NPT) because it hardly attracts trajectories within the neighborhood. The method of preventing the closed trajectory formation has been proposed on the basis of the NPT formation mechanism in this paper. The method has extended the period of NPT considerably. It is expected to apply security applications for IoT devices.
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Fast Stream Cipher based Chaos Neural Network for Data Security in CAN Bus
Zhongda Liu, Takeshi Murakami, Satoshi Kawamura, Hitoaki Yoshida
Advances in Science, Technology and Engineering Systems Journal 5 ( 5 ) 63 - 68 2020.09 [Refereed]
Bulletin of University, Institute, etc. Multiple authorship
The Controller Area Network (CAN) protocol is widely implemented due to its high fault tolerance. However, the CAN is a serial broadcast bus, and it has no protection against security threats. In this paper,
we propose a fast stream cipher based on a chaos neural network (CNN) that is able to generate pseudo-random numbers at a high speed, faster than that of the Advanced Encryption Standard,
to protect ECUs on the CAN bus by encrypting CAN messages. We discuss the chaotic orbit of the CNN and statistical properties of pseudo-random numbers from the CNN. For a stream
cipher, it is very important to share the symmetric key. We designed a symmetric key that is shared with ID-based encryption without the need to use digital certificates. We evaluated our method's performance with embedded boards and showed that the stream cipher is efficient for the embedded software of the ECU. Further, it does not need a hardware security module to accelerate the encryption. -
Implementation of High-Speed Pseudo-Random-Number Generator with Chaotic and Random Neural Networks
H. Yoshida, H. Fukuchi and T. Murakami
Proceedings of Papers, HICSS53 2020 6418 - 6425 2020.01 [Refereed]
Bulletin of University, Institute, etc. Multiple authorship
Chaotic and random time series generated from improved chaotic and random neural network (CRNN) afford statistically appropriate pseudo-random number series for information security. Randomness of outputs of CRNN is empirically validated in detail, and control methods of an appropriate ratio of chaotic character and randomness in the time series for PRNG is reported. The rate of random number generation has reached 2.8530×10^12 b/s. In future, the generator may play an important role on implementing applications for protecting personal information on the Internet.
Presentations 【 display / non-display 】
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Implementation of chaos neural network RNG based on Android mobile terminals
Oral Presentation(General)
2017.11 -
High-Speed and Highly Secure Pseudo-Random Number Generator based on Chaos Neural Network
Oral Presentation(General) Hitoaki YOSHIDA, Takeshi MURAKAMI and Zhongda LIU
2015.07 -
Study on Testing for Randomness of Pseudo-Random Number Sequence with NIST SP800-22 rev.1a
Oral Presentation(General) Hitoaki YOSHIDA, Takeshi MURAKAMI, and Satoshi KAWAMURA
2012.11
Academic Awards Received 【 display / non-display 】
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Best Paper Award (International Conference on System Science and Engineering (ICSSE-2015))
2015.08.08
All winners： Hitoaki YOSHIDA, Takeshi MURAKAMI and Zhongda LIU
Association Memberships 【 display / non-display 】
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2016.08
International Society of Applied Intelligence (ISAI)
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2011.01
International Association for Cryptologic Research
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2008.09
The Institute of Electronics, Information and Communication Engineers
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2004.04
Council for Improvement of Education through Computers
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2000.06
the Society of Instrument and Control Engineers