CSNN: Password guessing method based on Chinese syllables and neural network

Abstract

Password guessing attack is the most direct way to gain access to information systems. Using appropriate methods to generate password dictionary can effectively improve the hit rate of password guessing attacks. A Chinese syllables and Neural Network-based password generation method CSNN is proposed for Chinese password sets. This method treats Chinese Syllables as integral elements and uses them to parse and process passwords. The processed passwords are trained in Long Short-Term Memory Neural Network, and the trained model is used to generate password dictionaries (guessing sets). Long Short-Term Memory Neural Network is a kind of Recurrent Neural Network. In order to evaluate the effectiveness of CSNN, the hit rates of guessing sets generated by CSNN on target password sets (test sets) are compared with Probability Context-Free Grammar (PCFG) and 5th-order Markov Chain Model. In hit rate experiment, guessing sets of different scales were selected; the results show that the comprehensive performance of guessing sets generated by CSNN is better than PCFG and 5th-order Markov Chain Model. Compared with PCFG, different scales of CSNN guessing sets can improve up to 9% in hit rate on some test sets; compared with 5th-order Markov Chain Model, the best performance range of CSNN guessing sets is 105 to 106 guesses, and their hit rate increases range from 2.6% to 12.03%.