On Efficiency of Distributed Password Recovery

Matoušek, Petr; Hranický, Radek; Holkovič, Martin; Ryšavý, Ondřej

doi:10.15394/jdfsl.2016.1380

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…It is hard to hit a hash value calculated by a long random string for the salted hash [17]. To accelerate the attack speed, some researchers focus on improving the hash calculation speed to make attackers try faster [18], including designing a special application-specific integrated circuit (ASIC) to calculate hash functions and deploy the program on distributed computers [19][20][21][22]. Accelerating the guessing speed is crucial to making more guesses, but our research in this article focuses on generating high-quality passwords.…”

Section: Introductionmentioning

confidence: 99%

LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

Guo

Tan

Liu

et al. 2022

Sensors

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With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator–probabilistic context-free grammar (LPG–PCFG) based on PCFG. LPG–PCFG directionally increases the probability of low-probability passwords in the models’ distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG–PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG–PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.

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Section: Introductionmentioning

confidence: 99%

LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

Guo

Tan

Liu

et al. 2022

Sensors

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“…In the software implementation, traditional password recovery based on the CPU architecture is limited by its computational speed for cryptographic algorithms and can crack only passwords with low complexity. For GPUs, highthroughput password recovery algorithms such as SHA1 and MD5 are implemented by means of multicore parallel computing [7]- [9]. However, the GPU has high power consumption and a low energy efficiency ratio.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable and High-Efficiency Password Recovery Algorithms Based on HRCA

Feng

Chen

et al. 2021

IEEE Access

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Cryptographic algorithms are widely used in information security fields such as network protocol authentication and commercial encryption software. Password recovery based on the hash algorithm is an important means of electronic forensics, encrypted information restoration, illegal information filtering, and network security maintenance. The traditional password recovery system is based mainly on the CPU and GPU and has a low energy efficiency ratio and cracking efficiency and cannot meet highperformance computing requirements. To further improve the computational efficiency and application flexibility of password recovery algorithms, this paper proposes a reconfigurable computing kernel design method based on a hybrid reconfigurable computing array (HRCA). Through in-depth analysis of the hash algorithm, the basic computing kernel set is extracted, and the combination design is carried out from the unit kernel, interconnection and storage structure to reconstruct the hash algorithm to match the application with the appropriate structure. Second, combined with the pipeline technology, the full pipeline hash and high-speed password attack algorithms are optimized and implemented to meet the needs of highperformance computing. Finally, an advanced computing kernel library is established, and the combination of a computing kernel map from the control and communication levels to achieve multidimensional reconfigurable computing and an overall placement strategy is used to make full use of the chip resources to improve computational efficiency. The experimental results and analysis show that compared with traditional CPU and GPU methods, the password recovery algorithm designed in this paper has the highest cracking speeds at 78.22 times and 2.65 times that of the CPU and GPU, respectively, and the highest energy efficiency ratio is 25.88 times and 3.16 times that of the CPU and GPU, respectively. Furthermore, the recovery efficiency has been significantly improved and meets the requirements of high-performance password recovery computing.

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