Sooyong Jeong  Dowon Hong  Changho Seo

10.5626/JOK.2019.46.2.208

secure key exchange neural cryptography random walk learning majority attack

Key exchange is one of the major aspects in cryptography. Recently, compared to the existing key exchange schemes, more efficient key exchange schemes have been proposed based on neural network learning. After the first key exchange scheme based on neural network was proposed, various attack models have been suggested in security analysis. Hebbian learning rule is vulnerable to majority attack which is the most powerful attack. Anti Hebbian learning rule is secure against majority attack has a limitation in efficiency, so we can only use key exchange scheme based on random walk learning rule which is more secure and efficient than the others. However, if we use random walk learning rule, the efficiency which is advantage about neural cryptography is reduced than the other learning rules. In this paper we analyze random walk and neural cryptography, and we propose new learning rule which is more efficient than existing random walk learning rule. Also, we theoretically analyze about key exchange scheme which is uses new learning rule and verify the efficiency and security by implementing majority attack model.