Alphanumeric Passwords Research Articles

Sloth: Key Stretching and Deniable Encryption using Secure Elements on Smartphones

Privacy enhancing technologies must not only protect sensitive data in-transit, but also locally at-rest. For example, anonymity networks hide the sender and/or recipient of a message from network adversaries. However, if a participating device is physically captured, its owner can be pressured to give access to the stored conversations. Therefore, client software should allow the user to plausibly deny the existence of meaningful data. Since biometrics can be collected without consent and server-based authentication leaks metadata, implementations typically rely on memorable passwords for local authentication. Traditional password-based key stretching lacks a strict time guarantee due to the ease of parallelized password guessing by attackers. This paper introduces Sloth, a key stretching method leveraging the Secure Element (SE) commonly found in modern smartphones to provide a strict rate limit on password guessing. While this would be straightforward with full access to the SE, Android and iOS only provide a very limited API. Sloth utilizes the existing developer SE API and novel cryptographic constructions to build an effective rate-limit for password guessing on recent Android and iOS devices. Our approach ensures robust security even for short, randomly-generated, six-character alpha-numeric passwords against adversaries with virtually unlimited computing resources. Our solution is compatible with approximately 96% of iPhones and 45% of Android phones and Sloth seamlessly integrates without device or OS modifications, making it immediately usable by app developers today. We formally define the security of Sloth and evaluate its performance on various devices. Finally, we present HiddenSloth, a plausibly-deniable encryption scheme leveraging Sloth. It provides multi-snapshot resistance against adversaries who can covertly capture its on-disk content multiple times.

A Graphical Password Authentication System

Graphical passwords provide a promising alternative to traditional alphanumeric passwords. They are attractive since people usually remember pictures better than words.In this extended abstract, we propose a simple graphical password authentication system. We describe its operation with some examples, and highlight important aspects of the system.

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.

Deep learning based graphical password authentication approach against shoulder-surfing attacks

The password used to authenticate users is vulnerable to shoulder-surfing assaults, in which attackers directly observe users and steal their passwords without using any other technical upkeep. The graphical password system is regarded as a likely backup plan to the alphanumeric password system. Additionally, for system privacy and security, a number of programs make considerable use of the graphical password-based authentication method. The user chooses the image for the authentication procedure when using a graphical password. Furthermore, graphical password approaches are more secure than text-based password methods. In this paper, the effective graphical password authentication model, named as Deep Residual Network based Graphical Password is introduced. Generally, the graphical password authentication process includes three phases, namely registration, login, and authentication. The secret pass image selection and challenge set generation process is employed in the two-step registration process. The challenge set generation is mainly carried out based on the generation of decoy and pass images by performing an edge detection process. In addition, edge detection is performed using the Deep Residual Network classifier. The developed Deep Residual Network based Graphical Password algorithm outperformance than other existing graphical password authentication methods in terms of Information Retention Rate and Password Diversity Score of 0.1716 and 0.1643, respectively.

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

Abstract: A graphical password authentication is a form of authentication where users select a picture, image, or sequence as their password. Graphical passwords are an alternative to alphanumeric passwords in which users click on graphical images rather than typing alphanumeric characters to authenticate their identity. This method has gained popularity due to its ease of use and ability to provide better security than traditional alphanumeric passwords. This research paper provides a comprehensive study of graphical password authentication, including its history, types, advantages and disadvantages, security concerns, and future developments. The paper also includes a review of related research on graphical password authentication and its effectiveness in different settings.

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]

Click and Session based Captcha as Graphical Password Authentication Process using AI Technology

Abstract: Graphical passwords, two-factor authentication, and other ways are just a few of the many options we have for authentication. Text based protection. Codes are susceptible to a variety of assaults, including dictionary attacks. As a result, as part of the authentication procedure, in addition to the usual alpha-numeric password, we also utilise pictures with captchas. It also fixes the problems with the pass points of graphical password systems. Automated Turing Test for Computers and Humans that Is Completely Public (CAPTCHA) Users who have successfully authenticated must pass a test in order to access their individual mail accounts. Accounts that put the secrecy, reliability, and privacy of data at risk are attacked by bots and other malicious software. This was stopped by the invention of CAPTCHA.

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.

Prevention of Shoulder-Surfing Attack Using Shifting Condition with the Digraph Substitution Rules

Graphical passwords are implemented as an alternative scheme to replace alphanumeric passwords to help users to memorize their password. However, most of the graphical password systems are vulnerable to shoulder-surfing attack due to the usage of the visual interface. In this research, a method that uses shifting condition with digraph substitution rules is proposed to address shoulder-surfing attack problem. The proposed algorithm uses both password images and decoy images throughout the user authentication procedure to confuse adversaries from obtaining the password images via direct observation or watching from a recorded session. The pass-images generated by this suggested algorithm are random and can only be generated if the algorithm is fully understood. As a result, adversaries will have no clue to obtain the right password images to log in. A user study was undertaken to assess the proposed method's effectiveness to avoid shoulder-surfing attacks. The results of the user study indicate that the proposed approach can withstand shoulder-surfing attacks (both direct observation and video recording method).The proposed method was tested and the results showed that it is able to resist shoulder-surfing and frequency of occurrence analysis attacks. Moreover, the experience gained in this research can be pervaded the gap on the realm of knowledge of the graphical password.

Intelligent Security Model for Password Generation and Estimation Using Hand Gesture Features

Computer security depends mainly on passwords to protect human users from attackers. Therefore, manual and alphanumerical passwords are the most frequent type of computer authentication. However, creating these passwords has significant drawbacks. For example, users often tend to choose passwords based on personal information so that they can be memorable and therefore weak and guessable. In contrast, it is often difficult to remember if the password is difficult to guess. We propose an intelligent security model for password generation and estimation to address these problems using the ensemble learning approach and hand gesture features. This paper proposes two intelligent stages: the first is the password generation stage based on the ensemble learning approach and the proposed S-Box. The second is the password strength estimation stage, also based on the ensemble learning approach. Four well-known classifiers are used: Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest Tree (RFT), and AdaBoost applied on two datasets: MNIST images dataset and password strength dataset. The experimental results showed that the hand gesture and password strength classification processes accurately performed at 99% in AUC, Accuracy, F1-measures, Precision, and Recall. As a result, the extracted features of hand gestures will directly impact the complexity of generated passwords, which are very strong, hard to guess, and memorable.

Inertial-Measurement- Based Biometric Authentication of Handwritten Signature.

With increased reliance of digital storage for personal, financial, medical, and policy information, a greater demand for robust digital authentication and cybersecurity protection measures results. Current security options include alpha-numeric passwords, two factor authentication, and bio-metric options such as fingerprint or facial recognition. However, all of these methods are not without their drawbacks. This projects leverages the fact that the use of physical handwritten signatures is still prevalent in society, and the thoroughly trained process and motions of handwritten signatures is unique for every individual. Thus, a writing stylus that can authenticate its user via inertial signature detection is proposed, which classifies inertial measurement features for user identification. The current prototype consists of two triaxial accelerometers, one mounted at each of the stylus' terminal ends. Features extracted from how the pen is held, stroke styles, and writing speed can affect the stylus tip accelerations which leads to a unique signature detection and to deter forgery attacks. Novel, manual spatiotemporal features relating to such metrics were proposed and a multi-layer perceptron was utilized for binary classification. Results of a preliminary user study are promising with overall accuracy of 95.7%, sensitivity of 100%, and recall rate of 90%.

ENHANCED GRAPHICAL PASSWORD AUTHENTICATION SYSTEM IN BANKING SECTOR

A bank plays a essential role in people s life and security of the customer is the most important. In order to protect the user account the authentication must be secured. The generalmethod which is used is textual password. Graphical Password technique is used to enhance the security of the banking website and to provide an alternative to the alphanumeric password techniques. There is a lot of security breaches can take placein a banking transaction. The Proposed methodis implemented to secure the transactions by executing three step verification processes. Passwords at multiple levels in the banking system ensure higher security. It eradicates unauthorized users at the very initial stages. Cued Click Point technique, Message Digest 5(MD5) algorithm and Reverse One Time Password are used to implement the enhanced graphical password authentication system. Multi-level passwords are proven to be more secure as compared with the current authentication techniques.

Authentication By Grid Image Pattern

Abstract: This paper tries to point out flashy topics in an authentication system that works by having the user select from images, in a specific pattern (order), presented in a Graphical User Interface (GUI). So that the graphical password approach is called Graphical Image Authentication (GIA). The most common authentication method is to use alpha-numerical passwords. For example user tends to choose passwords that can be easily guessed on the other hand, if a password is difficult to guess then it is often difficult to remember. To overcome this problem of low security, Authentication methods change from images to passwords. Because humans can remember visuals (patterns) better than text, graphical password schemes have been offered as a feasible replacement to text-based systems. Visual patterns, rather than specific images, are easier to remember.Patterns are general easier to remembered or recognized than text. This early study implies that many graphics in graphical password systems may support memorability. Keywords: Authentication, Pattern based authentication, Click points, Grid images, Graphical passwords

Review Paper Graphical Password Authentication Technique for Security

Abstract: In modern age user authentication is a mandatory attribute in the area of data security. The characteristics of finding an individual generally based on a user id and password which is commonly used methodology for identifying users in system. Text based password is a general authentication method used from early times. There are numerous authentication systems like biometric, textual, graphical, smart card etc. Graphical password is an alternative to alphanumeric password which is tough to remember and generally forget by users as times passes. But in graphical password there is less probability to disremember password because people generally remember images more effortlessly than text based password. There are also fewer probabilities for intruder to take the graphical based password because hackers will not be capable to access the images uploaded by the user as password. Some graphical password schemes have been intended so far as it grows password usability and security. In this paper, we manner a wide survey of the current graphical password methods. Keywords: Graphical password, Text based password, usability, security, Attacks, Authentication

Detection of DIAG and LINE Patterns in PassPoints Graphical Passwords Based on the Maximum Angles of Their Delaunay Triangles.

An alternative authentication method to traditional alphanumeric passwords is graphical password authentication, also known as graphical authentication, for which one of the most valuable cued-recall techniques is PassPoints. This technique stands out for its security and usability. However, it can be violated if the user follows a predefined pattern when selecting the five points in an image as their passwords, such as the DIAG and LINE patterns. Dictionary attacks can be built using these two patterns to compromise graphical passwords. So far, no reports have been found in the state of the art about any test capable of detecting graphical passwords with DIAG or LINE patterns in PassPoints. Studies carried out in other scenarios have shown the effectiveness of the characteristics of Delaunay triangulations in extracting information about the dependence between the points. In this work, graphical passwords formed by five randomly selected points on an image are compared with passwords whose points contain patterns of the DIAG or LINE type. The comparison is based on building for each password its Delaunay triangulation and calculating the mean value of the maximum angles of the triangles obtained; such a mean value is denoted by amadt. It is experimentally shown that in passwords containing DIAG and LINE patterns, the value of amadt is higher than the one obtained in passwords formed by random dots. From this result, it is proposed to use this amadt value as a statistic to build a test of means. This result constitutes the work’s main contribution: The proposal of a spatial randomness test to detect weak graphic passwords that contain DIAG and LINE type patterns. The importance and novelty of this result become evident when two aspects are taken into account: First, these weak passwords can be exploited by attackers to improve the effectiveness of their attacks; second, there are no prior criteria to detect this type of weak password. The practical application of said test contributes to increasing PassPoints security without substantially affecting its efficiency.

GRA-PIN: A Graphical and PIN-Based Hybrid Authentication Approach for Smart Devices.

In many smart devices and numerous digital applications, authentication mechanisms are widely used to validate the legitimacy of users’ identification. As a result of the increased use of mobile devices, most people tend to save sensitive and secret information over such devices. Personal Identification Number (PIN)-based and alphanumeric passwords are simple to remember, but at the same time, they are vulnerable to hackers. Being difficult to guess and more user-friendly, graphical passwords have grown in popularity as an alternative to all such textual passwords. This paper describes an innovative, hybrid, and much more robust user authentication approach, named GRA-PIN (GRAphical and PIN-based), which combines the merits of both graphical and pin-based techniques. The feature of simple arithmetic operations (addition and subtraction) is incorporated in the proposed scheme, through which random passwords are generated for each login attempt. In the study, we have conducted a comparative study between the GRA-PIN scheme with existing PIN-based and pattern-based (swipe-based) authentications approaches using the standard Software Usability Scale (SUS). The usability score of GRA-PIN was analyzed to be as high as 94%, indicating that it is more reliable and user friendly. Furthermore, the security of the proposed scheme was challenged through an experiment wherein three different attackers, having a complete understanding of the proposed scheme, attempted to crack the technique via shoulder surfing, guessing, and camera attack, but they were unsuccessful.

Principles for Designing Authentication Mechanisms for Young Children: Lessons Learned from KidzPass

Young children routinely authenticate themselves with alphanumeric passwords but are probably not ready to use such passwords due to their emerging literacy and immaturity. They might adopt insecure coping tactics, which could become entrenched. Because children have a superior pictorial recognition ability, graphical authentication mechanisms will likely represent more suitable mechanisms for this demographic. We propose and study KidzPass, a configurable graphical authentication framework that one can use to tailor these mechanisms for children of different ages. We carried out two empirical investigations with four- to five-year-old children and with six- to seven-year-old children using personalized images as secrets (familiar faces and self-drawn doodles). KidzPass proved efficacious and our younger (four- to seven-year-old) participants mostly preferred it to text passwords. The personalized images maximize memorability but take significant time to obtain. As children mature, it might be possible to replace personalized images with generic images. Thus, we carried out a final empirical study with older children using generic images (that we chose). From this study, we found that that generic images can indeed be viable if they display particular qualities, which we enumerate. From our experiences and the research literature, we conclude by providing principles to inform efforts to design and evaluate age-appropriate authentication mechanisms for young children both from an ethical and technical perspective.

Web Application Authentication Using Visual Cryptography and Cued Clicked Point Recall-based Graphical Password

Alphanumerical usernames and passwords are the most used computer authentication technique. This approach has been found to have a number of disadvantages. Users, for example, frequently choose passwords that are simple to guess. On the other side, if a password is difficult to guess, it is also difficult to remember. Graphical passwords have been proposed in the literature as a potential alternative to alphanumerical passwords, based on the fact that people remember pictures better than text. Existing graphical passwords, on the other hand, are vulnerable to a shoulder surfing assault. To address this shoulder surfing vulnerability, this study proposes an authentication system for web-applications based on visual cryptography and cued click point recall-based graphical password. The efficiency of the proposed system was validated using unit, system and usability testing measures. The results of the system and unit testing showed that the proposed system accomplished its objectives and requirements. The results of the usability test showed that the proposed system is easy to use, friendly and highly secured.

Biometric keystroke barcoding: A next-gen authentication framework

Investigation of new intelligent solutions for user identification and authentication is and will be essential for enhancing the security of the alphanumeric passwords entered on touchscreen and traditional keyboards. Extraction of the keystrokes has been very beneficial given the intelligent authentication protocols operating in time-domain; while the time-domain solutions drastically lose their efficiency over time due to converging inter-key times. Realistically reflecting the habitual traits, the frequency-domain solutions, however, reveal unique biometric characteristics better, without any risk of convergence. On the contrary, the existing frequency-based frameworks don’t provide storable biometric data for further classification of the attempts. Therefore, we propose a novel barcoding framework converting habitual biometric information into storable barcodes as very low-size barcode images. The key-press times are extracted and turned into pseudo-signals exhibiting binary-train characteristics for continuous wavelet transformation (CWT). The transformed signals are primarily categorized with 4-scale scalograms by various complex frequency B-spline wavelets and subsequently superposed to create the unique barcodes. One-class support vector machines (SVM) is employed as the main classifier for training and testing the barcodes and very promising results are achieved given the lowest equal error rate (EER) of 1.83%.