CM-Droid: Secure Container for Android Password Misuse Vulnerability

Wen, Zhang; Li, Keyue; Li, Tianyang; Niu, Shaozhang; Gao, Zhenguang

doi:10.32604/cmc.2019.05813

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…is makes passwords vulnerable to guessing attacks and library crashing attacks. Once the attacker succeeds, a large amount of user private information will be leaked with unpredictable consequences [12][13][14]. For the purpose of protecting information security, it is critical to study password guessing methods.…”

Section: Introductionmentioning

confidence: 99%

Password Guessing Based on GAN with Gumbel-Softmax

Zhou

et al. 2022

Security and Communication Networks

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Password guessing is an important issue in user security and privacy protection. Using generative adversarial network (GAN) to guess passwords is a new strategy emerging in recent years, which exploits the discriminator’s evaluation of passwords to guide the update of the generator so that password guessing sets can be produced. However, the sampling process of discrete data from a categorical distribution is not differentiable so that backpropagation does not work well. In this paper, we propose a novel password guessing model named G-Pass, which consists of two main components. The first is a new network structure, which modifies the generator from the convolutional neural network (CNN) to long short-term memory- (LSTM-) based network and employs multiple convolutional layers in the discriminator to provide more informative signals for generator updating. The second is Gumbel-Softmax with temperature control for training GAN on passwords. Experimental results show the proposed G-Pass outperforms PassGAN in password quality and cracking rate. Moreover, by dynamically adjusting one parameter during the training process, a trade-off between sample diversity and quality can be achieved with our proposed model.

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Section: Introductionmentioning

confidence: 99%