计算机科学 ›› 2023, Vol. 50 ›› Issue (1): 334-341.doi: 10.11896/jsjkx.211100001
李小玲1, 吴昊天1, 周涛1, 鲁辉2
LI Xiaoling1, WU Haotian1, ZHOU Tao1, LU Hui2
摘要： 口令猜解是口令安全研究的重要方向之一。基于生成式对抗网络(Generative Adversarial Network,GAN)的口令猜解是近几年提出的一种新方法,其通过判别器对生成口令的评判结果来指导生成器的更新,进而生成口令猜测集。然而由于判别器对生成器的指导不足,现有的基于GAN的口令猜解模型的猜解效率较低。针对这个问题,提出了一种基于强化学习Actor-Critic算法改进的GAN口令猜解模型AC-Pass。AC-Pass模型通过Critic网络和判别器输出的奖赏共同指导Actor网络每一时间步生成策略的更新,实现了对口令序列生成过程的强化指导。将AC-Pass模型应用到RockYou,LinkedIn和CSDN口令集进行实验,并与PCFG模型、已有基于GAN的口令猜解模型PassGAN和seqGAN进行比较。实验结果表明,无论是同源测试集还是异源测试集,AC-Pass模型在9×108猜测集上的口令破解率均高于PassGAN和seqGAN;且当测试集与训练集之间的口令空间分布差异较大时,AC-Pass表现出了优于PCFG的口令猜解性能;另外,AC-Pass模型有较大的口令输出空间,其破解率随着口令猜测集的增大而提高。
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| [1]HAN W L,YUAN L,LI S S,et al.An Efficient Algorithm to Generate Password Sets Based on Samples[J].Chinese Journal of Computers,2017,40(5):1151-1167. [2]LIU G S,QIU W D,MENG K,et al.Password Vulnerability Assessment and Recovery Based on Ruels Mined from Large-Scale Real Data[J].Chinese Journal of Computers,2016,39(3):454-467. [3]XIE Z J,ZHANG M,LI Z H,et al.Analysis of Large-scale Real User Password Data Based on Cracking Algorithms[J].Computer Science,2020,47(11):48-54. [4]WANG D,ZOU Y K,TAO Y,et al.Password Guessing Model Based on Recurrent Neural Networks and Generative Adversa-rial Networks[J].Chinese Journal of Computers,2021,44(8):1519-1534. [5]YU L,ZHANG W,WANG J,et al.Seqgan:Sequence generative adversarial nets with policy gradient[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2017,31(1),2852-2858. [6]NARAYANAN A,SHMATIKOV V.Fast dictionary attacks on passwords using time-space tradeoff[C]//Proceedings of the 12th ACM Conference on Computer and communications security.2005:364-372. [7]WEIR M,AGGARWAL S,DE MEDEIROS B,et al.Password cracking using probabilistic context-free grammars[C]//2009 30th IEEE Symposium on Security and Privacy.IEEE,2009:391-405. [8]TANSEY W.Improved models for password guessing [EB/OL].https://www.semanticscholar.org/paper/ImprovedMo-dels-for-Password-Guessing-Tansey/3451ac7f102da12e1197c681b77d368ba3b19ac9. [9]DÜRMUTH M,ANGELSTORF F,CASTELLUCCIA C,et al.OMEN:Faster password guessing using an ordered markov enumerator[C]//International Symposium on Engineering Secure Software and Systems.Cham:Springer,2015:119-132. [10]HOUSHMAND S,AGGARWAL S,FLOOD R.Next gen PCFG password cracking [J].IEEE Transactions on Information Forensics and Security,2015,10(8):1776-1791. [11]WANG D,WANG P.The emperor's new password creationpolicies[C]//European Symposium on Research in Computer Security.Cham:Springer,2015:456-477. [12]LI Y,WANG H,SUN K.A study of personal information in human-chosen passwords and its security implications[C]//IEEE INFOCOM 2016-the 35th Annual IEEE International Confe-rence on Computer Communications.IEEE,2016:1-9. [13]WANG D,ZHANG Z,WANG P,et al.Targeted online password guessing:An underestimated threat[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security.2016:1242-1254. [14]MELICHER W,UR B,SEGRETI S M,et al.Fast,lean,and accurate:Modeling password guessability using neural networks[C]//25th {USENIX} Security Symposium({USENIX} Security 16).2016:175-191. [15]XU L,GE C,QIU W,et al.Password guessing based on LSTM recurrent neural networks[C]//2017 IEEE International Conference on Computational Science and Engineering(CSE) and IEEE International Conference on Embedded and Ubiquitous Computing(EUC).IEEE,2017:785-788. [16]XIA Z Y,YI P,LIU Y,et al.GENPass:A multi-source deeplearning model for password guessing[J].IEEE Transactions on Multimedia,2019,22(5):1323-1332. [17]HITAJ B,GASTI P,ATENIESE G,et al.Passgan:A deeplearning approach for password guessing[C]//International Conference on Applied Cryptography and Network Security.Cham:Springer,2019:217-237. [18]GULRAJANI I,AHMED F,ARJOVSKY M,et al.Improvedtraining of wasserstein gans [J].arXiv:1704.00028,2017. [19]NAM S,JEON S,KIM H,et al.Recurrent gans password cra-cker for iot password security enhancement [J].Sensors,2020,20(11):3106. [20]PASQUINI D,GANGWAL A,ATENIESE G,et al.Improving password guessing via representation learning[C]//2021 IEEE Symposium on Security and Privacy(SP).IEEE,2021:1382-1399. [21]MNIH V,KAVUKCUOGLU K,SILVER D,et al.Human-level control through deep reinforcement learning [J].Nature,2015,518(7540):529-533. [22]SILVER D,LEVER G,HEESS N,et al.Deterministic policygradient algorithms[C]//International Conference on Machine Learning.PMLR,2014:387-395. [23]KONDA V R,TSITSIKLIS J N.Actor-critic algorithms[C]//Advances in Neural Information Processing Systems.2000:1008-1014. [24]LILLICRAP T P,HUNT J J,PRITZEL A,et al.Continuouscontrol with deep reinforcement learning [J].arXiv:1509.02971,2015. [25]MNIH V,BADIA A P,MIRZA M,et al.Asynchronous methodsfor deep reinforcement learning[C]//International Conference on Machine Learning.PMLR,2016:1928-1937. [26]YANG S M,SHAN Z,DING Y,et al.Survey of Research on Deep Reinforcement Learning[J].Computer Engineering,2021,47(12):19-29. [27]LIN K,LI D,HE X,et al.Adversarial ranking for language ge-neration [J].arXiv:1705.11001,2017. [28]FEDUS W,GOODFELLOW I,DAI A M.Maskgan:better text generation via filling in the_ [J].arXiv:1801.07736,2018. [29]ZHANG X,LECUN Y.Text understanding from scratch [J].arXiv:1502.01710,2015. |
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