scholarly journals A Novel Session Password Security Technique using Textual Color and Images

2021 ◽  
Vol 1916 (1) ◽  
pp. 012176
Author(s):  
P Kavitha Rani ◽  
R Sai Krishna ◽  
U S Siddarth ◽  
E Vidya Sagar
Keyword(s):  
2016 ◽  
Vol 24 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Xiaoying Yu ◽  
Qi Liao

Purpose – Passwords have been designed to protect individual privacy and security and widely used in almost every area of our life. The strength of passwords is therefore critical to the security of our systems. However, due to the explosion of user accounts and increasing complexity of password rules, users are struggling to find ways to make up sufficiently secure yet easy-to-remember passwords. This paper aims to investigate whether there are repetitive patterns when users choose passwords and how such behaviors may affect us to rethink password security policy. Design/methodology/approach – The authors develop a model to formalize the password repetitive problem and design efficient algorithms to analyze the repeat patterns. To help security practitioners to analyze patterns, the authors design and implement a lightweight, Web-based visualization tool for interactive exploration of password data. Findings – Through case studies on a real-world leaked password data set, the authors demonstrate how the tool can be used to identify various interesting patterns, e.g. shorter substrings of the same type used to make up longer strings, which are then repeated to make up the final passwords, suggesting that the length requirement of password policy does not necessarily increase security. Originality/value – The contributions of this study are two-fold. First, the authors formalize the problem of password repetitive patterns by considering both short and long substrings and in both directions, which have not yet been considered in past. Efficient algorithms are developed and implemented that can analyze various repeat patterns quickly even in large data set. Second, the authors design and implement four novel visualization views that are particularly useful for exploration of password repeat patterns, i.e. the character frequency charts view, the short repeat heatmap view, the long repeat parallel coordinates view and the repeat word cloud view.


2021 ◽  
Author(s):  
Mathieu Christmann ◽  
Peter Mayer ◽  
Melanie Volkamer
Keyword(s):  

2005 ◽  
pp. 205-224 ◽  
Author(s):  
Jan L. Harrington
Keyword(s):  

2013 ◽  
Vol 3 (2) ◽  
pp. 58-70 ◽  
Author(s):  
B. Dawn Medlin

Due to the Internet and applications that can access the Internet, healthcare employees can benefit from the ability to view patient data almost anywhere and at any time. Data and information is also being shared among third party vendors, partners and supplies. With this type of accessibility of information which generally does include very personal information such as diagnosis and social security numbers, data can easily be obtained either through social engineering techniques or weak password usage. In this paper, a presentation of social engineering techniques is explored as well as the password practices of actual health care workers.


Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3106 ◽  
Author(s):  
Sungyup Nam ◽  
Seungho Jeon ◽  
Hongkyo Kim ◽  
Jongsub Moon

Text-based passwords are a fundamental and popular means of authentication. Password authentication can be simply implemented because it does not require any equipment, unlike biometric authentication, and it relies only on the users’ memory. This reliance on memory is a weakness of passwords, and people therefore usually use easy-to-remember passwords, such as “iloveyou1234”. However, these sample passwords are not difficult to crack. The default passwords of IoT also are text-based passwords and are easy to crack. This weakness enables free password cracking tools such as Hashcat and JtR to execute millions of cracking attempts per second. Finally, this weakness creates a security hole in networks by giving hackers access to an IoT device easily. Research has been conducted to better exploit weak passwords to improve password-cracking performance. The Markov model and probabilistic context-free-grammar (PCFG) are representative research results, and PassGAN, which uses generative adversarial networks (GANs), was recently introduced. These advanced password cracking techniques contribute to the development of better password strength checkers. We studied some methods of improving the performance of PassGAN, and developed two approaches for better password cracking: the first was changing the convolutional neural network (CNN)-based improved Wasserstein GAN (IWGAN) cost function to an RNN-based cost function; the second was employing the dual-discriminator GAN structure. In the password cracking performance experiments, our models showed 10–15% better performance than PassGAN. Through additional performance experiments with PCFG, we identified the cracking performance advantages of PassGAN and our models over PCFG. Finally, we prove that our models enhanced password strength estimation through a comparison with zxcvbn.


Preview ◽  
2019 ◽  
Vol 2019 (199) ◽  
pp. 38-38 ◽  
Author(s):  
Dave Annetts
Keyword(s):  

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