The Experts below are selected from a list of 1686 Experts worldwide ranked by ideXlab platform

Perez-cruz Fernando - One of the best experts on this subject based on the ideXlab platform.

  • PassGAN: A Deep Learning Approach for Password Guessing
    'Springer Science and Business Media LLC', 2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, creating and expanding them to model further Passwords is a labor-intensive task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode

  • PassGAN: A Deep Learning Approach for Password Guessing
    2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further Passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd

Hitaj Briland - One of the best experts on this subject based on the ideXlab platform.

  • PassGAN: A Deep Learning Approach for Password Guessing
    'Springer Science and Business Media LLC', 2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, creating and expanding them to model further Passwords is a labor-intensive task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode

  • PassGAN: A Deep Learning Approach for Password Guessing
    2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further Passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd

Fernando Perez-cruz - One of the best experts on this subject based on the ideXlab platform.

  • PassGAN: A Deep Learning Approach for Password Guessing
    arXiv: Cryptography and Security, 2017
    Co-Authors: Briland Hitaj, Giuseppe Ateniese, Paolo Gasti, Fernando Perez-cruz
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further Passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode.

Gasti Paolo - One of the best experts on this subject based on the ideXlab platform.

  • PassGAN: A Deep Learning Approach for Password Guessing
    'Springer Science and Business Media LLC', 2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, creating and expanding them to model further Passwords is a labor-intensive task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode

  • PassGAN: A Deep Learning Approach for Password Guessing
    2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further Passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd

Ateniese Giuseppe - One of the best experts on this subject based on the ideXlab platform.

  • PassGAN: A Deep Learning Approach for Password Guessing
    'Springer Science and Business Media LLC', 2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, creating and expanding them to model further Passwords is a labor-intensive task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode

  • PassGAN: A Deep Learning Approach for Password Guessing
    2019
    Co-Authors: Hitaj Briland, Ateniese Giuseppe, Gasti Paolo, Perez-cruz Fernando
    Abstract:

    State-of-the-art Password guessing tools, such as HashCat and John the Ripper, enable users to check billions of Passwords per second against Password hashes. In addition to performing straightforward dictionary attacks, these tools can expand Password dictionaries using Password generation rules, such as concatenation of words (e.g., "Password123456") and leet speak (e.g., "Password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further Passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-Generated Password rules with theory-grounded machine learning algorithms. Instead of relying on manual Password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real Passwords from actual Password leaks, and to generate high-quality Password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large Password datasets, we were able to surpass rule-based and state-of-the-art machine learning Password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on Passwords or common Password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more Passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of Password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd