System Password Security: Attack and Defense Mechanisms
- URL: http://arxiv.org/abs/2510.10246v1
- Date: Sat, 11 Oct 2025 15:01:58 GMT
- Title: System Password Security: Attack and Defense Mechanisms
- Authors: Chaofang Shi, Zhongwen Li, Xiaoqi Li,
- Abstract summary: In recent years, frequent password cracking attacks targeting system passwords have posed a severe threat to information system security.<n>This paper conducts systematic research on system password security, focusing on analyzing typical password cracking methods.<n>It also evaluates widely adopted defense mechanisms, including account lockout policies, multi-factor authentication, and risk adaptive authentication.
- Score: 4.188145506259564
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: System passwords serve as critical credentials for user authentication and access control when logging into operating systems or applications. Upon entering a valid password, users pass verification to access system resources and execute corresponding operations. In recent years, frequent password cracking attacks targeting system passwords have posed a severe threat to information system security. To address this challenge, in-depth research into password cracking attack methods and defensive technologies holds significant importance. This paper conducts systematic research on system password security, focusing on analyzing typical password cracking methods such as brute force attacks, dictionary attacks, and rainbow table attacks, while evaluating the effectiveness of existing defensive measures. The experimental section utilizes common cryptanalysis tools, such as John the Ripper and Hashcat, to simulate brute force and dictionary attacks. Five test datasets, each generated using Message Digest Algorithm 5 (MD5), Secure Hash Algorithm 256-bit (SHA 256), and bcrypt hash functions, are analyzed. By comparing the overall performance of different hash algorithms and password complexity strategies against these attacks, the effectiveness of defensive measures such as salting and slow hashing algorithms is validated. Building upon this foundation, this paper further evaluates widely adopted defense mechanisms, including account lockout policies, multi-factor authentication, and risk adaptive authentication. By integrating experimental data with recent research findings, it analyzes the strengths and limitations of each approach while proposing feasible improvement recommendations and optimization strategies.
Related papers
- DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective [70.77570343385928]
We introduce a novel taxonomy, classifying existing methods based on their reliance on internal features (IF) (inherent to the data) versus external features (EF) (artificially introduced for auditing)<n>We formulate two primary attack types: evasion attacks, designed to conceal the use of a dataset, and forgery attacks, intending to falsely implicate an unused dataset.<n>Building on the understanding of existing methods and attack objectives, we further propose systematic attack strategies: decoupling, removal, and detection for evasion; adversarial example-based methods for forgery.<n>Our benchmark, DATABench, comprises 17 evasion attacks, 5 forgery attacks, and 9
arXiv Detail & Related papers (2025-07-08T03:07:15Z) - Optimizing Password Cracking for Digital Investigations [0.0]
This study explores rule based optimisation strategies to enhance the effectiveness of password cracking.<n>We propose an optimised rule set that reduces computational iterations by approximately 40%.<n>We evaluate the crackability of three word passwords using dictionaries of varying common word proportions.
arXiv Detail & Related papers (2025-04-04T11:03:39Z) - Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem.<n>These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem.<n>We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
arXiv Detail & Related papers (2025-01-25T04:53:36Z) - Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption [0.4711628883579317]
This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption.
It examines their encoding techniques such as advanced encryp-tion standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20.
The results showed that ChaCha20 had the best average time for both encryp-tion and decryption, being over 50% faster than some other algorithms.
arXiv Detail & Related papers (2024-07-23T08:26:05Z) - Cryptanalysis and improvement of multimodal data encryption by
machine-learning-based system [0.0]
encryption algorithms to accommodate varied requirements of this field.
Best approach to analyzing an encryption algorithm is to identify a practical and efficient technique to break it.
arXiv Detail & Related papers (2024-02-24T10:02:21Z) - PassGPT: Password Modeling and (Guided) Generation with Large Language
Models [59.11160990637616]
We present PassGPT, a large language model trained on password leaks for password generation.
We also introduce the concept of guided password generation, where we leverage PassGPT sampling procedure to generate passwords matching arbitrary constraints.
arXiv Detail & Related papers (2023-06-02T13:49:53Z) - On Deep Learning in Password Guessing, a Survey [4.1499725848998965]
This paper compares various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations.
We propose a promising research experimental design on using variations of IWGAN on password guessing under non-targeted offline attacks.
arXiv Detail & Related papers (2022-08-22T15:48:35Z) - GNPassGAN: Improved Generative Adversarial Networks For Trawling Offline
Password Guessing [5.165256397719443]
This paper reviews various deep learning-based password guessing approaches.
It also introduces GNPassGAN, a password guessing tool built on generative adversarial networks for trawling offline attacks.
In comparison to the state-of-the-art PassGAN model, GNPassGAN is capable of guessing 88.03% more passwords and generating 31.69% fewer duplicates.
arXiv Detail & Related papers (2022-08-14T23:51:52Z) - Recovering AES Keys with a Deep Cold Boot Attack [91.22679787578438]
Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down.
In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys.
Our results show that our methods outperform the state of the art attack methods by a very large margin.
arXiv Detail & Related papers (2021-06-09T07:57:01Z) - MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks.
The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
arXiv Detail & Related papers (2020-10-25T23:01:13Z) - Targeted Attack for Deep Hashing based Retrieval [57.582221494035856]
We propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
We first formulate the targeted attack as a point-to-set optimization, which minimizes the average distance between the hash code of an adversarial example and those of a set of objects with the target label.
To balance the performance and perceptibility, we propose to minimize the Hamming distance between the hash code of the adversarial example and the anchor code under the $ellinfty$ restriction on the perturbation.
arXiv Detail & Related papers (2020-04-15T08:36:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.