Textual Passwords Research Articles

Enhancing Data Warehouse Security: An Encrypted Cued-Click-Points Authentication Approach

Text passwords and other traditional security measures have been the mainstay of current data warehouse content security techniques. Due to the growing computational capability of information systems, security requirements, especially for the selection of secure text passwords, have changed throughout time. Because database administrators are either unaware of these changes or are overburdened by the magnitude of the adjustments necessary to remedy them, these vulnerabilities are typically invisible to them. As a result, research on usable authentication schemes have picked up steam, with the goal of developing a secure authentication method using cued click points and encryption techniques for accessing a hybrid data warehouse. This scheme aims to enhance the security of data access by combining the unique characteristics of cued click points with encryption to create a robust authentication system. The project will involve designing and implementing the authentication scheme, testing its effectiveness in a hybrid data warehouse environment, and evaluating its performance in terms of security and usability. Overall, the project aims to provide a secure and user-friendly authentication solution for accessing sensitive data in a hybrid data warehouse setting. This scheme utilizes a combination of encryption techniques and user-specific click points to verify user identity and access to the data warehouse. It also discusses the potential benefits of this authentication scheme in terms of data security and user experience.

Securing Access: A Combined Approach of Cued Click Points and Text Passwords for Authentication

Securing Access: A Combined Approach of Cued Click Points and Text Passwords for Authentication

CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords

Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication. Sequential MFA requires a number of steps to be followed in sequence for authentication; for example, with three factors, the system requires three authentication steps. In this case, to proceed with MFA using a deep learning approach, three artificial neural networks (ANNs) are needed. In contrast, in parallel MFA, the authentication steps are processed simultaneously. This means that processing is possible with only one ANN. A convolutional neural network (CNN) is a method for learning images through the use of convolutional layers, and researchers have proposed several systems for MFA using CNNs in which various modalities have been employed, such as images, handwritten text for authentication, and multi-image data for machine learning of facial emotion. This study proposes a CNN-based parallel MFA system that uses concatenation. The three factors used for learning are a face image, an image converted from a password, and a specific image designated by the user. In addition, a secure password image is created at different bit-positions, enabling the user to securely hide their password information. Furthermore, users designate a specific image other than their face as an auxiliary image, which could be a photo of their pet dog or favorite fruit, or an image of one of their possessions, such as a car. In this way, authentication is rendered possible through learning the three factors—that is, the face, password, and specific auxiliary image—using the CNN. The contribution that this study makes to the existing body of knowledge is demonstrating that the development of an MFA system using a lightweight, mobile, multi-factor CNN (MMCNN), which can even be used in mobile devices due to its low number of parameters, is possible. Furthermore, an algorithm that can securely transform a text password into an image is proposed, and it is demonstrated that the three considered factors have the same weight of information for authentication based on the false acceptance rate (FAR) values experimentally obtained with the proposed system.

PASS POINT SELECTION OF AUTOMATIC GRAPHICAL PASSWORD AUTHENTICATION TECHNIQUE BASED ON HISTOGRAM METHOD

Graphical passwords, as opposed to textual passwords, require the user to pick pictures or draw symbols rather than input written letters. They are an option that may be explored in order to get over the issues that are caused by the system of passwords that are based on text. It has been hypothesized that graphical passwords are more difficult to crack using a brute force technique or to figure out through guessing. This paper proposes an authentication system based on a graphical password method. The proposed system computes the password points using histogram arithmetic and encrypts the chosen password points using SHA512. The envisioned system has been realized as an android application and evaluated with existing research considering multiple measurements such as required login time, password space, and entropy. The findings reveal that the new suggested system outperforms the reference work by more than 85% in terms of login latency and more than 72% in terms of entropy results.

Graphical Password Scheme Based on Color Hint Approach

Generally, textual password is the most used kind of authentication technique. However, this authentication type is vulnerable to a lot of attacks. Users using this kind of authentication techniques are at the risk of exposing vital organizational data or even password to attacks such as phishing, shoulder surfing, brute force and dictionary attacks etc. This is the major reason why a new authentication type is required, in other to minimize these threats or attacks, graphical authentication techniques were developed. But this authentication also faces attacks such as shoulder surfing and guessing attacks etc. In this paper, a graphical password scheme based on color hint approach has been developed and analyzed. The scheme authenticates users in two different ways which are: using an image to select different password click points and using secret color hint and transparent shapes as a way for the user to know the position of the click points after it had been reshuffled on the selected image. The project will be limited to the reliability, efficiency, ease of use and friendliness of graphical user interface during the user authentication. The system has been able to minimize or reduce the problem of shoulder surfing and guessing attacks.

Combination of Alphanumeric Password and Graphic Authentication for Cyber Security

The main objective of this research is to create a more secure and user-friendly password-generation method that is less vulnerable to attacks such as shoulder surfing. Similarly, while textual or alphanumeric passwords are not entirely secure, they also come with certain limitations, including issues related to memory. The novelty of this research is the development of a new algorithm (Secure Password Algorithm/SPA) for password generation that uses graphics to generate textual input from users based on user perceptions. This method is more resilient against attacks like shoulder surfing and offers a more secure and user-friendly way of creating passwords. This research uses a combination of literature review and SPA proposals to create passwords with graphical and encryption techniques. The literature review examines the weaknesses of existing graphical and text password methods. The proposed algorithm focuses on user perception and confidential data to create more secure and easy-to-use passwords. The research results show that this research can provide a new, more secure, and user-friendly method of generating passwords such as shoulder surfing. Research supports software implementation of these algorithms and psychometric, psychological, and psychiatric studies to improve user input and protect systems from malicious attacks. Graphical passwords provide better recall compared to textual passwords, and the proposed algorithm for password generation using graphical and encryption techniques provides a more secure and user-friendly method of password generation that is less vulnerable to attacks such as shoulder surfing. The proposed new encryption algorithm has succeeded in overcoming various potential weaknesses and attacks on previous encryption methods. The test results demonstrate the efficiency and near invulnerability of the SPA algorithm against brute force attacks on specific security parameters. Tested alongside AES, shows comparable security levels, particularly in resource-constrained environments. To address doubts, the paper establishes evaluation standards for computational complexity, resistance to known attacks, and adaptability to evolving threats. The algorithm has the potential to be a stronger and more secure solution for generating passwords to safeguard confidential user information.

THE LANDSCAPE OF AUTHENTICATION SYSTEMS: A COMPREHENSIVE SURVEY

Nowadays, authentication systems are extremely important in many aspects of digital life. They help to protect personal and confidential data from unauthorized access. authentication systems are effective ways to prevent hacking and fraud attacks. Also, They are crucial in ensuring digital security and protecting sensitive data in our connected world. The authentication can be done in multiple ways, one of which is using a password, such system is called a password-based authentication system. Password is the basic and most common method of authentication. This paper overviews the authentication systems, types, and objectives and mentions each type's disadvantages, advantages, and problems. The paper then highlights the text password authentication system, its weak points, and attacks they are exposed to, in addition to developing means and methods to increase the strength of passwords so that they can overcome the attacks they face. The paper provides a good background for anyone who wants to start in this field, as it gives a detailed idea of the systems used, making it easier for the reader to choose the authentication system to desire

Password cracking using chunk similarity

Textual password is one of the most important authentic means used in modern information systems and password cracking is an important means of measuring password strength. Recently, data-driven approaches are proposed in order to improve the efficiency and accuracy of the password guessing process. These methods usually train a model based on leaked password datasets so as to capture the internal patterns hidden behind the human created passwords, and most of them focus on the relationships between characters within a password. In this article, we emphasizes that the character relations between passwords need also to be considered. We treat a password as a sequence of chunks or segments, which is a small sub-string of the password and appears frequently in a password dataset. Instead of modeling the relations of chunks within a password, we proposed a method, which selects a seed password from a training set, breaks the seed password into chunks, and then generates new passwords by choosing a chunk and replacing it with another one according to their similarities. Several experiments are conducted on three password datasets so as to evaluate different aspects of the proposed approach. The results show that the proposed method is comparable with the state-of-the-art approaches, such as PassGAN, DPG, FLA and PCFG v4.3, which is the latest version of PCFG. The results also revealed that chunk level relations between passwords play an important role in the process of password creation.

Authentication Based on Image Password

Abstract: In the modern day, graphical password authentication that relies on memory or recognition is a different and still developing sort of authentication. The user copies the picture or is able to identify the image that was used or made during the registration process. Only authorised users are able to access resources and information thanks to passwords. The newest fashion is graphic passwords, which appear to be a highly promising authentication technique. It is available as a substitute for text passwords. The most popular method is using usernames and passwords that are alphanumeric. While this method has certain benefits over plain text passwords, it also has some drawbacks. Users strive to memorise short, strong passwords that can be broken when using alphanumeric passwords, but doing so becomes cumbersome. Users that use graphical passwords save a picture or certain areas of an image as their password. Biometrics is now the most sophisticated and secure form of authentication, yet it is too costly for widespread usage. Graphical passwords are a fantastic alternative authentication mechanism since they are less expensive, more secure, and simpler for everyone to use. [1]

Authentication Based on Image Password

Abstract: In the modern day, graphical password authentication that relies on memory or recognition is a different and still developing sort of authentication. The user copies the picture or is able to identify the image that was used or made during the registration process. Only authorised users are able to access resources and information thanks to passwords. The newest fashion is graphic passwords, which appear to be a highly promising authentication technique. It is available as a substitute for text passwords. The most popular method is using usernames and passwords that are alphanumeric. While this method has certain benefits over plain text passwords, it also has some drawbacks. Users strive to memorise short, strong passwords that can be broken when using alphanumeric passwords, but doing so becomes cumbersome. Users that use graphical passwords save a picture or certain areas of an image as their password. Biometrics is now the most sophisticated and secure form of authentication, yet it is too costly for widespread usage. Graphical passwords are a fantastic alternative authentication mechanism since they are less expensive, more secure, and simpler for everyone to use. [1]

Graphical Password Authentication: Image Grid Based Digital Lock for Mobile Apps

Abstract: Over the past few decades, research on authentication methods has advanced significantly. While the initial focus was on using standardised password selection and management approaches to secure textual passwords, this was made possible by learning about the flaws in the existing systems based on the attacks performed on the same systems' vulnerabilities. With the rise of biometric-based authentication, the next stage in the study and development of improved solutions has begun. Although initially rather entertaining, this authentication method is very insecure. Once a person's biometrics have been stolen or replicated, new biometrics cannot be made. Compared to a person's one-step biometric authentication, biometrics in the multifactor authentication of an enterprise are less vulnerable. The development of graphical passwords is the result of all these issues with authentication systems. While multifactor graphical passwords impede smartphone users' usability and speedier processing-computational flexibility, overly simplistic graphical passwords are prone to shoulder surfing. Users of Android devices need an authentication system that is easy to use, quick, secure, and that gains faster access to the device. A study was done evaluating the usability, predictability, resistance to various attacks, and other characteristics of various graphical passwords. Grid-based graphical passwords outperformed their rivals among the different types of graphical passwords that were taken into consideration. The majority of graphical password implementations in the past have been on personal computers (PCs), while smartphone usage is far more prevalent than on PCs. We propose to develop an Android application with gridbased image-based graphical password authentication that will be secure and help a lot of users protect the secondary identities that they carry about in the form of smartphones. After evaluating which factors must remain in the system, the application was divided into five modules. Each module was first built using flowcharts, and later, the Java programming language was used to build the app in Android Studio.

A Comparison Between Position-Based and Image-Based Multi-Layer Graphical user Authentication System

System security is very important, especially in the age that we live in. One of the ways to secure data is by creating a password that makes it difficult for unauthorized user to gain access to the system. However, what makes it difficult for the system to be attacked is directly dependent on approach used to create it, and how secured it is. Text based approach is the oldest authentication approach. It requires that the user supplies textual password in order to gain access to the system. However, this approach has shown a significant drawback and several vulnerabilities, one of which is the difficulty in recalling or remembering textual passwords. Several other attacks that textual passwords are vulnerable to include brute force attacks, shoulder spying, dictionary attacks etc. The introduction of graphical schemes made things a lot better. Graphical passwords make use of images. However, most graphical schemes are vulnerable to shoulder surfing attacks. In this research work, we developed two systems; A position-based multi-layer graphical user authentication system and an Image-based multi-layer graphical user authentication system. The reason behind this research work is to compare the two systems, and evaluate them based on three major performance metrics: (1) Security, (2) Reliability (3) Individual preference.

A Comparison of a Touch-Gesture- and a Keystroke-Based Password Method: Toward Shoulder-Surfing Resistant Mobile User Authentication

The pervasive use of mobile devices exposes users to an elevated risk of shoulder-surfing attacks. Despite the prior work on shoulder-surfing resistance of mobile user authentication methods, there is a lack of empirical studies on textual password authentication methods, particularly the hybrid passwords that integrate textual passwords with biometrics. To fill the literature gap, this research compares two hybrid password methods, touch-gesture- and keystroke-based passwords, with respect to their shoulder-surfing resistance performance. We select a touch-gesture-based password method that deploys multiple shoulder-surfing resistance strategies and a keystroke-based password method that leverages keystroke dynamics. To gain a holistic understanding of these password methods, we examine them under a variety of shoulder-surfing settings by varying interaction mode, observation angle, entry error, and observation effort. Going beyond effectiveness metrics, we also introduce efficiency metrics to assess shoulder-surfing resistance performance more comprehensively. We hypothesize and test the effects of shoulder-surfing settings by conducting both a longitudinal lab experiment and an online experiment with diversified participants. The results of both studies demonstrate the superior performance of the touch-gesture-based password method to the keystroke-based counterpart. The results also provide evidence for the effects of interaction mode, observation angle, and observation effort on shoulder-surfing resistance of hybrid passwords. Our findings offer suggestions for the design and strategies for strengthening the security of password authentication methods.

Seamless Security on Mobile Devices Textual Password Quantification Model Based Usability Evaluation of Secure Rotary Entry Pad Authentication

Mobile devices are vulnerable to shoulder surfing and smudge attacks, which should occur when a user enters a PIN for authentication purposes. This attack can be avoided by implementing a rotary entry pad mechanism. Despite this, several studies have found that using a rotary entry pad reduces user usability. This study uses a Design Research Methodology approach. It will implement a rotary entry pad authentication in the Android operating system as an authentication method to protect the device against Shoulder Surfing Attacks and Smudge Attacks. Furthermore, it combined JSON Web Token (JWT) to secure the authentication process from the client to the server. At the end of implementation, it compared with other studies in terms of usability and evaluated it using the TQ-Model, which showed that the usability aspect has improved. Regarding security, we conducted a shoulder surfing attack simulation to assess the efficacy of guessing PINs. The results showed that only a limited number of attempts were successful, with two out of five samples failing to guess any numbers and only one sample successfully guessing six 10-digit PIN combinations out of 10 to the power of 10. The security test results show that shoulder surfing attacks are more difficult to perform after implementing the rotary entry pad. The evaluation showed that the JSpinpad performed better, with seven parameters showing improvement, one parameter showing a decline, and ten parameters remaining unchanged.

Facial Image Encryption for Secure Face Recognition System

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.

Strengthen Password Using Image Authentication

Authentication is by password It is alphanumeric in nature. However, the user finds it Difficulty remembering long passwords Remember many times while running. instead of this. They create short, simple and insecure passwords. User data vulnerable to external attacks. Graphically Passwords offer a way out of this dilemma Passwords that are easy for users to remember and use Passwords, more secure. With Graphical password, user clicks on image instead of typing it. A text password containing alphanumeric characters. A New, more secure graphical password system Developed using image segmentation. Picture A segmentation system presents images to the user Here the user selects some grid on that image. If These points, entered in the correct order, user. The result is an alphanumeric password and Both graphical passwords worked around the same time, Graphical passwords were easy to obtain and remember. [1]As such, graphical passwords have been found to be harder to crack They are newly implemented and don't have many algorithms Designed to break them.

Security and Usability of a Personalized User Authentication Paradigm: Insights from a Longitudinal Study with Three Healthcare Organizations

This article proposes a user-adaptable and personalized authentication paradigm for healthcare organizations, which anticipates to seamlessly reflect patients’ episodic and autobiographical memories to graphical and textual passwords aiming to improve the security strength of user-selected passwords and provide a positive user experience. We report on a longitudinal study that spanned over 3 years in which three public European healthcare organizations participated to design and evaluate the aforementioned paradigm. Three studies were conducted ( n = 169) with different stakeholders: (1) a verification study aiming to identify existing authentication practices of the three healthcare organizations with diverse stakeholders ( n = 9), (2) a patient-centric feasibility study during which users interacted with the proposed authentication system ( n = 68), and (3) a human guessing attack study focusing on vulnerabilities among people sharing common experiences within location-aware images used for graphical passwords ( n = 92). Results revealed that the suggested paradigm scored high with regard to users’ likeability, perceived security, usability, and trust, but more importantly it assists the creation of more secure passwords. On the downside, the suggested paradigm introduces password guessing vulnerabilities by individuals sharing common experiences with the end users. Findings are expected to scaffold the design of more patient-centric knowledge-based authentication mechanisms within today's dynamic computation realms.

PGTCN: A novel password-guessing model based on temporal convolution network

Text passwords are the primary method for identity authentication. However, easy-to-remember passwords are vulnerable to password-guessing attacks. The study of password-guessing not only enhances the understanding of password security, but also promotes the improvement of password library security. Nowadays, deep learning based models have demonstrated their promising ability for password-guessing, e.g., Recurrent Neural Network (RNN) and its variants. However, RNNs are failed to parallelize and lack long memory, resulting in unsatisfactory guessing efficiency and effectiveness. Aiming to generate a high-quality password dictionary for password checking, we propose a temporal convolutional network-based password-guessing model named PGTCN. Specifically, in PGTCN we improve the password-guessing efficiency by introducing the Improved Temporal Convolutional Network (ITCN) which adopts residual learning and feature fusion techniques. PGTCN can automatically study the structure and characteristics of passwords and generate new passwords based on the learned knowledge. To verify the performance of PGTCN, we compare it with state-of-the-art models on six public password datasets. Evaluation results show that the password dictionary generated by the proposed PGTCN achieves the structure coverage rate up to 84%, and boosts the matching rate up to 22%. We conclude that the PGTCN could be regarded as a robust and efficient model for password guessing.

A Novel Multi-Factor Authentication Algorithm Based on Image Recognition and User Established Relations

Conventional authentication methods, like simple text-based passwords, have shown vulnerabilities to different types of security attacks. Indeed, 61% of all breaches involve credentials, whether stolen via social engineering or hacked using brute force. Therefore, a robust user authentication mechanism is crucial to have secure systems. Combining textual passwords with graphical passwords in a multi-factor approach can be an effective strategy. Advanced authentication systems, such as biometrics, are secure, but require additional infrastructure for efficient implementation. This paper proposes a Multi-Factor Authentication (MFA) based on a non-biometric mechanism that does not require additional hardware. The novelty of the proposed mechanism lies in a two-factor authentication algorithm which requires a user to identify specific images out of a set of randomly selected images, then the user is required to establish a self-pre-configured relation between two given images to complete authentication. A functional prototype of the proposed system was developed and deployed. The proposed system was tested by users of different backgrounds achieving 100% accuracy in identifying and authenticating users, if authentication elements and credentials were not forgotten. It was also found to be accepted by the users as being easy to use and preferable over common MFA mechanisms.

OMECDN: A Password-Generation Model Based on an Ordered Markov Enumerator and Critic Discriminant Network

At present, static text passwords are still the most widely-used identity authentication method. Password-generation technology can generate large-scale password sets and then detect the defects in password-protection mechanisms, which is of great significance for evaluating password-guessing algorithms. However, the existing password-generation technology cannot ignore low-quality passwords in the generated password set, which will lead to low-efficiency password guessing. In this paper, a password-generation model based on an ordered Markov enumerator and critic discriminant network (OMECDN) is proposed, where passwords are generated via an ordered Markov enumerator (OMEN) and a discriminant network according to the probability of the combination of passwords. OMECDN optimizes the performance of password generation with a discriminative network based on the good statistical properties of OMEN. Moreover, the final password set is formed by the selected passwords with a higher score than the preset threshold, which guarantees the superiority of the hit rate of almost all ranges of combinations of passwords over the initial password set. Finally, the experiments show that OMECDN achieves a qualitative improvement in hit rate metrics. In particular, regarding the generation of 107 passwords on the RockYou dataset, the matching entries of the password set generated by the OMECDN model are 25.18% and 243.58% higher than those generated by the OMEN model and the PassGAN model, respectively.