• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.6
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• pp.71-80
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• 2002

To provide the confidentiality of messages transmitted over the network, the use of cryptographic system is increasing gradually and the hybrid cryptosystem, which combines the advantages of the symmetric cryptosystem and the public key cryptosystem is widely used. In this paper, we proposes a new hybrid cryptosystem capable of providing implicit authentication for the sender of the ciphertext by means of the 1-pass key distribution protocol that offers implicit key authentication, hash function and symmetric cryptosystem. Also, we describe some examples such as the Diffie-Hellman based system and the Nyberg-Ruppel based system. The proposed hybrid cryptosystem is an efficient more than general public key cryptosystems in the aspect of computation work and provides implicit authentication for the sender without additional increase of the communication overhead.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.107-109
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• 2022

This paper proposes a new security system to detect cyber-attacks based on message authentication in a in-vehicle network. In the in-vehicle network, when a sending node transmits messages in a broadcast manner, it only uses a message identifier, rather than a node's identifier. It leads to a problem not identifying the source. In the proposed system, the sending node generates a message authentication code (MAC) using a cryptographic hash function to the control data and transmits it with the control data. When generating the MAC for each message, a multidimensional chaotic map is applied to increase the randomness of the result. The receiving node compares its MAC generated from the control data in the received message with the MAC of the received message to detect whether the message transmitted from the sending node is forged or not. We evaluate the performance of the proposed system by using CANoe and CAPL (Communication Access Programming Language). Our system shows a 100% of detection rate against cyber-attacks injected.