This paper presents an advanced keystroke authentication model improving users’ validation strength. The
proposed system is based on defining a keystroke structure for each authorized user, to be used in the user
login attempts. This structure is composed based on two components; the user’s typing time deviation
thresholds; and a unique user secret code which is distributed between password's characters based on
time distances. The strength of the proposed method depends primarily on the amount of information
distributed among typing time, and on reducing the deviation of these times. During the preliminary
evaluation, it was confirmed that the proposed system has achieved an improved authentication level, and
the system model was highly accepted between participating users.
Keystroke Dynamics Authentication with Project Management SystemIJSRD
Generally user authentication is done using username and password that is called as login process. This login process is not more secure because, however a login session is still unprotected to impersonator when the user leaves his computer without logging off. Keystroke dynamics methods can be made useful to verify a user by extracting some typing features then, after the authentication process has successfully ended. From the last decade several studies proposed the use of keystroke dynamics as a behavioral biometric tool to verify users. We propose a new method, for representing the keystroke patterns by joining similar pairs of consecutive keystrokes. The above proposed method is used to consider clustering the di-graphs which are based on their temporal features. In this project, authentication system is provide to project management system that make more Secure management system without acknowledging unauthorized user. The Project Management System addresses the management of software projects. It provides the framework for organizing and managing resources in such a way that these resources deliver all the work required to complete a software project within defined scope, time and cost constraints. The system applies only to the management of software projects and is a tool that facilitates decision making.
Keystroke Dynamics Support For AuthenticationMayank Sachan
This document provides an outline and overview of keystroke dynamics for user authentication. It discusses:
- Biometrics like fingerprints and keystroke patterns that can be used for identification
- Keystroke dynamics measures latency between keystrokes and duration of key presses
- Literature on keystroke authentication systems, classifiers, and factors that affect performance
- Approaches like static and dynamic verification, and features like hold time and up-down times
- System architecture including enrollment, extraction, preprocessing, storage, and authentication
- The document presents results of experiments analyzing EER, accuracy of different features, and comparisons of authentication attempts.
Keystroke dynamics is a behavioral biometric technology that authenticates users based on their unique typing rhythm. It provides a solution to common password vulnerabilities like passwords being shared or stolen. The technology originated from analyzing the unique rhythms of Morse code operators during World War II. It is a software-only solution that does not require special devices and can integrate easily with existing systems. A company called BIOPASSWORD provides keystroke dynamics products and solutions to help organizations secure access and comply with privacy regulations.
Keystroke dynamics, or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard.
This document describes the design of a unique security system using fingerprint authentication and passwords. The system uses a fingerprint sensor and password entry for verification before automatically unlocking an entry door. It also includes a camera that takes photos when invalid entries are made, and a GSM module that sends entry information to the owner. The system aims to provide high security for places requiring unique identity verification like offices, banks, and nuclear facilities.
Biometrics uses measurements of physical and behavioral characteristics to authenticate identity. During enrollment, a biometric sample is taken and stored in a database. When authenticating, a new sample is taken and matched against stored data. If the samples match within a set threshold, access is granted. Common biometric traits include fingerprints, iris scans, facial recognition, voice recognition, and hand geometry. While effective, biometrics raise privacy and security concerns if biometric data is stolen or shared without consent. Implementation also faces challenges of user acceptance, high costs, and accuracy limitations. Overall, biometrics can reliably verify identities but organizations must consider implications and mitigate risks to privacy.
The document discusses using handwritten signature verification as an additional security measure for computer systems. It notes that signature verification must be cheap, reliable, and unobtrusive. It explains that online signature verification analyzes dynamic features of signing, like speed and pressure, which presents challenges in differentiating consistent versus varying behavioral elements of a person's signature over time. The document outlines the signature acquisition and identification process using global features and training models, and notes the benefits of low error rates, ability to detect forgery even with copied signatures, fast training, and cheap storage requirements.
Biometric encryption is a technique that securely binds a cryptographic key to a biometric template. During enrollment, a random key is linked to a user's biometric template using a binding algorithm. During verification, applying the same biometric will regenerate the key through a retrieval algorithm. This allows authentication while preventing storage of the actual biometric data. Biometric encryption offers advantages like improved security, ability to revoke compromised keys, and privacy by not retaining biometric images.
Keystroke Dynamics Authentication with Project Management SystemIJSRD
Generally user authentication is done using username and password that is called as login process. This login process is not more secure because, however a login session is still unprotected to impersonator when the user leaves his computer without logging off. Keystroke dynamics methods can be made useful to verify a user by extracting some typing features then, after the authentication process has successfully ended. From the last decade several studies proposed the use of keystroke dynamics as a behavioral biometric tool to verify users. We propose a new method, for representing the keystroke patterns by joining similar pairs of consecutive keystrokes. The above proposed method is used to consider clustering the di-graphs which are based on their temporal features. In this project, authentication system is provide to project management system that make more Secure management system without acknowledging unauthorized user. The Project Management System addresses the management of software projects. It provides the framework for organizing and managing resources in such a way that these resources deliver all the work required to complete a software project within defined scope, time and cost constraints. The system applies only to the management of software projects and is a tool that facilitates decision making.
Keystroke Dynamics Support For AuthenticationMayank Sachan
This document provides an outline and overview of keystroke dynamics for user authentication. It discusses:
- Biometrics like fingerprints and keystroke patterns that can be used for identification
- Keystroke dynamics measures latency between keystrokes and duration of key presses
- Literature on keystroke authentication systems, classifiers, and factors that affect performance
- Approaches like static and dynamic verification, and features like hold time and up-down times
- System architecture including enrollment, extraction, preprocessing, storage, and authentication
- The document presents results of experiments analyzing EER, accuracy of different features, and comparisons of authentication attempts.
Keystroke dynamics is a behavioral biometric technology that authenticates users based on their unique typing rhythm. It provides a solution to common password vulnerabilities like passwords being shared or stolen. The technology originated from analyzing the unique rhythms of Morse code operators during World War II. It is a software-only solution that does not require special devices and can integrate easily with existing systems. A company called BIOPASSWORD provides keystroke dynamics products and solutions to help organizations secure access and comply with privacy regulations.
Keystroke dynamics, or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard.
This document describes the design of a unique security system using fingerprint authentication and passwords. The system uses a fingerprint sensor and password entry for verification before automatically unlocking an entry door. It also includes a camera that takes photos when invalid entries are made, and a GSM module that sends entry information to the owner. The system aims to provide high security for places requiring unique identity verification like offices, banks, and nuclear facilities.
Biometrics uses measurements of physical and behavioral characteristics to authenticate identity. During enrollment, a biometric sample is taken and stored in a database. When authenticating, a new sample is taken and matched against stored data. If the samples match within a set threshold, access is granted. Common biometric traits include fingerprints, iris scans, facial recognition, voice recognition, and hand geometry. While effective, biometrics raise privacy and security concerns if biometric data is stolen or shared without consent. Implementation also faces challenges of user acceptance, high costs, and accuracy limitations. Overall, biometrics can reliably verify identities but organizations must consider implications and mitigate risks to privacy.
The document discusses using handwritten signature verification as an additional security measure for computer systems. It notes that signature verification must be cheap, reliable, and unobtrusive. It explains that online signature verification analyzes dynamic features of signing, like speed and pressure, which presents challenges in differentiating consistent versus varying behavioral elements of a person's signature over time. The document outlines the signature acquisition and identification process using global features and training models, and notes the benefits of low error rates, ability to detect forgery even with copied signatures, fast training, and cheap storage requirements.
Biometric encryption is a technique that securely binds a cryptographic key to a biometric template. During enrollment, a random key is linked to a user's biometric template using a binding algorithm. During verification, applying the same biometric will regenerate the key through a retrieval algorithm. This allows authentication while preventing storage of the actual biometric data. Biometric encryption offers advantages like improved security, ability to revoke compromised keys, and privacy by not retaining biometric images.
The document discusses different types of biometric authentication methods including physiological traits like fingerprints, iris scans, and facial recognition as well as behavioral traits like voice recognition and signatures. It explains that biometric authentication works by acquiring a user's unique characteristics, creating a digital template, storing that template, and then comparing subsequent scans to that template for verification. The document also outlines some common applications of biometric authentication technologies.
A secure Crypto-biometric verification protocol Nishmitha B
This document describes blind authentication, a secure crypto-biometric verification protocol. It discusses privacy concerns with traditional biometric systems and introduces blind authentication as a way to conduct biometric verification without revealing biometric samples or classifier details. The key features of blind authentication are described, including how enrollment and authentication work based on homomorphic encryption. Experimental results demonstrate the efficiency and accuracy of the proposed approach.
This document discusses biometric security for mobile devices. It covers various biometric authentication methods like facial recognition, fingerprints, and voice recognition. It analyzes the pros and cons of each method in terms of accuracy, security, usability, and other factors. The document also provides examples of how voice biometrics can be used for fast authentication and describes the VoiceKeyID algorithm which can authenticate users with half a second of speech.
This document discusses various biometric identification techniques including 3D facial recognition, iris recognition, and keystroke dynamics. It provides details on how each method works, advantages, limitations, and applications. 3D facial recognition involves capturing a facial image and transforming it into a unique face print using elastic graph matching algorithms to allow identification from several meters away. Iris recognition uses the colored iris surrounding the eye which is stable over a person's lifetime allowing identification with low error rates. Keystroke dynamics analyzes typing patterns such as keystroke duration and pressure to continuously authenticate computer users with minimal hardware requirements.
Biometrics refers to authentication techniques that rely on measurable physiological and behavioral characteristics to verify identity. A biometric system automatically recognizes individuals based on characteristics like fingerprints, facial features, iris patterns, etc. There are two types of identity resolution in biometric systems - verification and identification. Verification compares a sample to a single stored template, while identification searches a sample against a database of templates. Biometric systems collect and process samples, extract distinguishing features, create templates, and make identity decisions based on template matches. Biometrics are increasingly used for security applications like access control and transactions.
This document discusses key considerations for protecting critical infrastructure from cybersecurity threats involving biometrics. It notes that while biometrics can strengthen security, biometric systems themselves must be secured against attacks. The document outlines vulnerabilities across different stages of biometric systems and recommends countermeasures like multi-factor authentication, flexible technology, and ongoing analysis to adapt to evolving threats. The overall message is that cybersecurity requires a holistic defense-in-depth approach when using biometrics to authenticate identity.
This document provides an overview of biometric systems security and privacy. It defines biometrics and describes the stages of biometric identification and verification. It discusses types of biometrics like fingerprints, iris scans, and behavioral biometrics. The document outlines privacy assessments for biometric systems and security vulnerabilities like sensor attacks, replay attacks, and template modification. It also describes methods for template protection including biometric cryptosystems, cancellable biometrics, and hybrid approaches. Finally, it discusses privacy benefits in multimodal biometric systems that use multiple biometrics.
This document provides an overview of access control, including identification, authentication, and authorization. It discusses different types of access controls like administrative, technical, and physical controls. It also covers specific access control methods like passwords, biometrics, smart cards, and tokens. Identification establishes a subject's identity, while authentication proves the identity. Authorization then controls the subject's access to resources based on their proven identity. The document categorizes access controls as preventive, detective, corrective, recovery, compensating, and directive. It provides examples of different administrative, technical, and physical controls that fall into each category.
In the age of Biometric Security taking over the traditional security features, this is a small intro to the Biometric features one can use to enhance the security. The various modalities have been explained.
User Identity Verification via Mouse DynamicsOuzza Brahim
1. The document proposes a novel method for continuously verifying user identity based on characteristics of their mouse interactions.
2. It introduces extracting features from individual mouse actions, in contrast to previous histogram approaches requiring aggregation of many actions.
3. The proposed algorithm is shown to outperform state-of-the-art methods by achieving higher verification accuracy while reducing response time.
The document provides several examples of access control from different authors' experiences:
1) Access cards, biometrics, usernames/passwords, and RADIUS are mentioned as common examples of access control for physical and network access.
2) Biometric systems, mechanical locks, and passive infrared request-to-exit devices are described as specific access control methods.
3) Building access control using ID cards, private document permissions, and shared server file access are outlined.
A keystroke dynamic is based on the assumption that each person has a unique keystroke rhythm.
KBBASs can be distinguished according to training data they use: 1.Static(Fixed) Text
2.NonStatic(Free) Text
Biometrics is the science of measuring and analyzing human body characteristics for authentication purposes. Major biometrics include face, fingerprints, and irises. Fingerprints are uniquely persistent and can be used for positive identification through analysis of ridge and minutiae patterns, such as bifurcations and ridge endings. Fingerprint images are typically stored and classified according to international standards that define format, resolution, and quality parameters.
This slideshow is about biometrics. I covered the examples and also its whole functioning as well the cryptography used in the biometric system. The Advantages and disadvantages as well.
This document discusses biometrics as an advanced technology for security and authentication. It describes different methods of biometric verification including fingerprint, retina scan, DNA analysis, typing patterns, signatures, and voice recognition. Biometrics can be used for identification by searching biometric databases or for verification by matching a submitted biometric to an existing record. While biometrics provide strong security, they also have limitations such as high costs, storage requirements, and potential for data loss affecting the security system.
The document describes a project to develop an access control and authentication system using RFID and fingerprint technologies. The system uses a microcontroller, RFID reader, fingerprint scanner and LCD. It combines "what you have" (RFID tag) and "who you are" (fingerprint) authentication. The objectives are to create a user-friendly system that can authenticate and validate access. The system was implemented and observations were made for valid and invalid RFID tags. It was concluded that the system can be used for verification and authentication in various applications.
Biometrics refers to authentication techniques that rely on measurable physical characteristics. There are several types of biometric identification including face, fingerprints, hand geometry, retina, iris, signature, and voice. Biometric characteristics can be physiological, related to the body, or behavioral, related to a person's actions. A biometric system works by enrolling individuals through storing biometric information, then detecting and comparing live biometrics during subsequent uses. Common biometric technologies include fingerprint scans, iris scans, and hand scans. Biometrics are used for physical access control, computer authentication, financial security, and other applications.
Protecting what needs to be protected with the available technologies!
• Access control is the heart of Information Security!
• The right
• Flow of information between subject and object
• Mechanism to protect the assets!
The Four Major Elements are:
Identification,
Authentication,
Authorization and main is CONTROLLING !!
Authentication is the process of verifying that a user is who they claim to be when attempting to access a system. There are two main authentication methods: biometric devices and callback systems. Biometric devices use unique personal characteristics like fingerprints, facial features, iris scans, and signatures to digitally identify users. Common biometric devices analyze fingerprints, facial structures, hand measurements, eye iris patterns, retinal scans, voice patterns, and signatures. Authentication helps prevent unauthorized access to systems and protects users' private data.
The EVSSO method involves:
1) Determining the level of maturity of an e-voting system's security using checklists.
2) Visualizing the current security situation to identify areas for improvement.
3) Comparing different e-voting systems to evaluate their optimization potential.
Biometric Security advantages and disadvantagesPrabh Jeet
Biometrics refers to authentication techniques that rely on measurable physiological and individual characteristics to automatically verify identity. A biometric system uses behavioral or biological traits like fingerprints, iris scans, or voice to identify or verify individuals. Identification involves searching a biometric sample against a database of templates, while verification compares a sample to a single stored template. Biometrics are increasingly used for security applications like access control and transactions due to their convenience and effectiveness compared to traditional authentication methods.
This document discusses user models and predictive modeling techniques for interface design. It covers Fitts' law for predicting pointing and selection times based on target size and distance. The keystroke-level model is presented for estimating times of complex tasks as the sum of basic operations. Guiard's model of bimanual control and neural networks are described as ways to predict more complex interactions like menu placement. Predictive models allow mathematical performance predictions while descriptive models provide frameworks for understanding problems. Both have limitations and should be used alongside user testing.
The document discusses different types of biometric authentication methods including physiological traits like fingerprints, iris scans, and facial recognition as well as behavioral traits like voice recognition and signatures. It explains that biometric authentication works by acquiring a user's unique characteristics, creating a digital template, storing that template, and then comparing subsequent scans to that template for verification. The document also outlines some common applications of biometric authentication technologies.
A secure Crypto-biometric verification protocol Nishmitha B
This document describes blind authentication, a secure crypto-biometric verification protocol. It discusses privacy concerns with traditional biometric systems and introduces blind authentication as a way to conduct biometric verification without revealing biometric samples or classifier details. The key features of blind authentication are described, including how enrollment and authentication work based on homomorphic encryption. Experimental results demonstrate the efficiency and accuracy of the proposed approach.
This document discusses biometric security for mobile devices. It covers various biometric authentication methods like facial recognition, fingerprints, and voice recognition. It analyzes the pros and cons of each method in terms of accuracy, security, usability, and other factors. The document also provides examples of how voice biometrics can be used for fast authentication and describes the VoiceKeyID algorithm which can authenticate users with half a second of speech.
This document discusses various biometric identification techniques including 3D facial recognition, iris recognition, and keystroke dynamics. It provides details on how each method works, advantages, limitations, and applications. 3D facial recognition involves capturing a facial image and transforming it into a unique face print using elastic graph matching algorithms to allow identification from several meters away. Iris recognition uses the colored iris surrounding the eye which is stable over a person's lifetime allowing identification with low error rates. Keystroke dynamics analyzes typing patterns such as keystroke duration and pressure to continuously authenticate computer users with minimal hardware requirements.
Biometrics refers to authentication techniques that rely on measurable physiological and behavioral characteristics to verify identity. A biometric system automatically recognizes individuals based on characteristics like fingerprints, facial features, iris patterns, etc. There are two types of identity resolution in biometric systems - verification and identification. Verification compares a sample to a single stored template, while identification searches a sample against a database of templates. Biometric systems collect and process samples, extract distinguishing features, create templates, and make identity decisions based on template matches. Biometrics are increasingly used for security applications like access control and transactions.
This document discusses key considerations for protecting critical infrastructure from cybersecurity threats involving biometrics. It notes that while biometrics can strengthen security, biometric systems themselves must be secured against attacks. The document outlines vulnerabilities across different stages of biometric systems and recommends countermeasures like multi-factor authentication, flexible technology, and ongoing analysis to adapt to evolving threats. The overall message is that cybersecurity requires a holistic defense-in-depth approach when using biometrics to authenticate identity.
This document provides an overview of biometric systems security and privacy. It defines biometrics and describes the stages of biometric identification and verification. It discusses types of biometrics like fingerprints, iris scans, and behavioral biometrics. The document outlines privacy assessments for biometric systems and security vulnerabilities like sensor attacks, replay attacks, and template modification. It also describes methods for template protection including biometric cryptosystems, cancellable biometrics, and hybrid approaches. Finally, it discusses privacy benefits in multimodal biometric systems that use multiple biometrics.
This document provides an overview of access control, including identification, authentication, and authorization. It discusses different types of access controls like administrative, technical, and physical controls. It also covers specific access control methods like passwords, biometrics, smart cards, and tokens. Identification establishes a subject's identity, while authentication proves the identity. Authorization then controls the subject's access to resources based on their proven identity. The document categorizes access controls as preventive, detective, corrective, recovery, compensating, and directive. It provides examples of different administrative, technical, and physical controls that fall into each category.
In the age of Biometric Security taking over the traditional security features, this is a small intro to the Biometric features one can use to enhance the security. The various modalities have been explained.
User Identity Verification via Mouse DynamicsOuzza Brahim
1. The document proposes a novel method for continuously verifying user identity based on characteristics of their mouse interactions.
2. It introduces extracting features from individual mouse actions, in contrast to previous histogram approaches requiring aggregation of many actions.
3. The proposed algorithm is shown to outperform state-of-the-art methods by achieving higher verification accuracy while reducing response time.
The document provides several examples of access control from different authors' experiences:
1) Access cards, biometrics, usernames/passwords, and RADIUS are mentioned as common examples of access control for physical and network access.
2) Biometric systems, mechanical locks, and passive infrared request-to-exit devices are described as specific access control methods.
3) Building access control using ID cards, private document permissions, and shared server file access are outlined.
A keystroke dynamic is based on the assumption that each person has a unique keystroke rhythm.
KBBASs can be distinguished according to training data they use: 1.Static(Fixed) Text
2.NonStatic(Free) Text
Biometrics is the science of measuring and analyzing human body characteristics for authentication purposes. Major biometrics include face, fingerprints, and irises. Fingerprints are uniquely persistent and can be used for positive identification through analysis of ridge and minutiae patterns, such as bifurcations and ridge endings. Fingerprint images are typically stored and classified according to international standards that define format, resolution, and quality parameters.
This slideshow is about biometrics. I covered the examples and also its whole functioning as well the cryptography used in the biometric system. The Advantages and disadvantages as well.
This document discusses biometrics as an advanced technology for security and authentication. It describes different methods of biometric verification including fingerprint, retina scan, DNA analysis, typing patterns, signatures, and voice recognition. Biometrics can be used for identification by searching biometric databases or for verification by matching a submitted biometric to an existing record. While biometrics provide strong security, they also have limitations such as high costs, storage requirements, and potential for data loss affecting the security system.
The document describes a project to develop an access control and authentication system using RFID and fingerprint technologies. The system uses a microcontroller, RFID reader, fingerprint scanner and LCD. It combines "what you have" (RFID tag) and "who you are" (fingerprint) authentication. The objectives are to create a user-friendly system that can authenticate and validate access. The system was implemented and observations were made for valid and invalid RFID tags. It was concluded that the system can be used for verification and authentication in various applications.
Biometrics refers to authentication techniques that rely on measurable physical characteristics. There are several types of biometric identification including face, fingerprints, hand geometry, retina, iris, signature, and voice. Biometric characteristics can be physiological, related to the body, or behavioral, related to a person's actions. A biometric system works by enrolling individuals through storing biometric information, then detecting and comparing live biometrics during subsequent uses. Common biometric technologies include fingerprint scans, iris scans, and hand scans. Biometrics are used for physical access control, computer authentication, financial security, and other applications.
Protecting what needs to be protected with the available technologies!
• Access control is the heart of Information Security!
• The right
• Flow of information between subject and object
• Mechanism to protect the assets!
The Four Major Elements are:
Identification,
Authentication,
Authorization and main is CONTROLLING !!
Authentication is the process of verifying that a user is who they claim to be when attempting to access a system. There are two main authentication methods: biometric devices and callback systems. Biometric devices use unique personal characteristics like fingerprints, facial features, iris scans, and signatures to digitally identify users. Common biometric devices analyze fingerprints, facial structures, hand measurements, eye iris patterns, retinal scans, voice patterns, and signatures. Authentication helps prevent unauthorized access to systems and protects users' private data.
The EVSSO method involves:
1) Determining the level of maturity of an e-voting system's security using checklists.
2) Visualizing the current security situation to identify areas for improvement.
3) Comparing different e-voting systems to evaluate their optimization potential.
Biometric Security advantages and disadvantagesPrabh Jeet
Biometrics refers to authentication techniques that rely on measurable physiological and individual characteristics to automatically verify identity. A biometric system uses behavioral or biological traits like fingerprints, iris scans, or voice to identify or verify individuals. Identification involves searching a biometric sample against a database of templates, while verification compares a sample to a single stored template. Biometrics are increasingly used for security applications like access control and transactions due to their convenience and effectiveness compared to traditional authentication methods.
This document discusses user models and predictive modeling techniques for interface design. It covers Fitts' law for predicting pointing and selection times based on target size and distance. The keystroke-level model is presented for estimating times of complex tasks as the sum of basic operations. Guiard's model of bimanual control and neural networks are described as ways to predict more complex interactions like menu placement. Predictive models allow mathematical performance predictions while descriptive models provide frameworks for understanding problems. Both have limitations and should be used alongside user testing.
This document summarizes a research paper that proposes using keystroke dynamics authentication with principal component analysis and neural networks. The paper extracts timing features from users' keystrokes and uses principal component analysis to reduce the dimensionality of the features before training a multi-layer perceptron neural network classifier. The results showed that using principal component analysis improved the neural network's performance by decreasing the false rejection and false acceptance rates, mean square error, and training time.
The document summarizes an evaluation of an automated teller machine (ATM) system. It analyzes the tasks users perform with ATMs, the typical user group, and methods used in the evaluation. It identifies critical defects like untrustworthy graphics and confusing navigation. Positives include the regularity of tasks and minimal learning curve. Negatives are misleading titles, too many button options, and lack of location awareness. Overall conclusions are the system is usable but could be improved significantly. Recommendations include better titles, graphics, touchscreens, intuitive actions, and location awareness.
This document provides an introduction to GOMS (Goals, Operators, Methods, and Selection rules), a usability analysis technique for quantitatively comparing the efficiency of different interface designs. It explains that GOMS involves defining a task, breaking it down into basic gestures like keystrokes and mouse clicks, applying mental operators, and adding up the time estimates for each step. An example compares the time taken to enter a date of birth on Facebook, Yahoo, and a single text field, finding the single field takes around half the time of the other options. The document encourages using GOMS alongside other qualitative usability tools and provides resources for learning more about the technique.
Top-10 Tips for Writing a Paper provides advice for effectively communicating research ideas in writing. The tips include developing a clear story or motivation for the work, writing with a top-down structure from broad concepts to specifics, crafting an engaging introduction that pitches the key ideas, using organizational techniques like paragraphs and signposts to guide the reader, putting oneself in the reader's perspective to ensure clarity and interest, focusing on the most important results and implications, stating limitations to avoid overstating findings, studying exemplary writing in the field, and allowing sufficient time for writing, reviewing, and improving the paper.
Cognitive-Perceptual-Motor GOMS Model of Human Computer InteractionShruti Nimbkar
This document discusses different GOMS models used to analyze user behavior. It specifically explains the CPM-GOMS model, which was developed in 1988 by Bonnie John and is based on the Card, Moran, and Newell model. The CPM-GOMS model assumes perceptual, cognitive, and motor operations can occur in parallel and models multitasking behavior that experienced users can exhibit.
Countermeasure against Timing Attack on SSH Using Random Delay - Arief Karfia...idsecconf
This document proposes adding random delays to packets sent over SSH connections in order to protect against timing attacks. It summarizes that SSH is currently vulnerable to timing attacks where an attacker can analyze inter-keystroke timing to infer passwords. The paper then describes implementing random delays between 0-300 milliseconds added to packets sent in SSH. Evaluations show the standard deviation of inter-keystroke times increases by about 14% after adding random delays, destroying the statistical patterns needed for successful timing attacks. The paper concludes random delays effectively counter timing attacks on SSH by randomizing inter-keystroke timing statistics.
Keystroke biometrics analyzes typing patterns to identify individuals. It is a behavioral biometric that does not require specialized hardware. bioChec provides a patented keystroke biometric software engine called bioChecKey that can integrate into various applications. It also offers online and SaaS solutions like bioChec Online! that can enhance security for websites and intranets. bioChec's solutions are minimally invasive and easily deployable for large user bases.
This document provides information about tools for organizing and managing research. It discusses Mendeley, a reference manager that allows researchers to collect, organize, and publish citations. It also covers RefMe, a citation tool that generates references and bibliographies with a click. The document then explains Ginger, a grammar checking tool, and Viper, an anti-plagiarism scanner. Finally, it discusses research networks like Google Scholar, Microsoft Academic Search, and ResearchGate that help manage publications and measure impact.
This document proposes a new technique for segmenting the region of interest (ROI) from finger-knuckle-print images. The technique uses gradient orientation and field strength to detect the center of the ROI. It was tested on 502 images from a public database with a 96.21% accuracy rate. The average processing time was 110 milliseconds per image, showing it can be implemented in real-time systems. The technique provides an effective and efficient approach for preprocessing finger-knuckle-print images by identifying the ROI prior to feature extraction and matching.
This document discusses adaptive behavior and higher cognitive functions from a multidisciplinary perspective, focusing on the social factors that make humans unique. It compares humans, chimpanzees, and rhesus macaques in terms of genetics, brain structure, and social intelligence. While humans and chimpanzees share more genetic similarities, rhesus macaques have social behaviors more like humans. The field of social neuroscience examines how the brain mediates social interactions and behaviors through structures involved in mentalizing and empathizing. Understanding primate social organizations provides insights into the evolution of human societies driven by social intelligence.
Signature recognition using clustering techniques dissertatiDr. Vinayak Bharadi
This document summarizes Vinayak Ashok Bharadi's dissertation on signature recognition using clustering techniques. It introduces the topic, outlines the problem definition and steps in signature recognition. It then discusses several preprocessing techniques, feature extraction methods like global features, grid and texture information, vector quantization, Walsh coefficients, and successive geometric centers. The document presents results and concludes by discussing the application of clustering techniques to signature recognition.
This document discusses three key aspects of designing for human error:
1) It examines human capabilities and limitations from physical, cognitive, and social perspectives to understand how design can fit with them.
2) It explores the difference between human errors like mistakes and slips versus design errors, noting people will err and design should account for this.
3) It outlines three approaches to design - technology-oriented, human-centered, and activity-centered - recommending the latter which develops a deep understanding of the activities to be performed.
Hyperspectral face recognition by texture feature extraction using hybrid wav...Dr. Vinayak Bharadi
The document proposes a system for hyperspectral face recognition using hybrid wavelet transforms. It introduces hyperspectral images and face recognition. The proposed system applies hybrid wavelet type I, type II, and Kekre wavelet transforms to hyperspectral face images to extract texture features and generate feature vectors. These feature vectors are stored in a database and analyzed using intra-class and inter-class testing to evaluate metrics like true acceptance rate, true rejection rate, and performance index for the different transform methods. Results show the hybrid wavelet type II transform achieved the highest performance index and security performance index for the right side face images compared to other transforms and instances.
Keystroke Dynamics Authentication with Project Management SystemIJSRD
This document summarizes a research paper on a proposed keystroke dynamics authentication system integrated with a project management system. The system aims to add an extra layer of security beyond typical username and password authentication. It extracts typing features from users during a training phase and verifies users during login based on their keystroke patterns. If verification fails after three attempts, it moves to additional authentication steps like one-time passwords and image selection. The system is meant to securely manage project information and files shared between employees of a project management system. Previous research on keystroke dynamics authentication is also summarized.
This document provides an overview of Human Information Processing (HIP) models in human-computer interaction (HCI). It discusses 1) what HIP is as a cognitive model that uses the computer as a metaphor for human cognitive functioning, 2) how HIP models are used in HCI to predict human-computer interaction, focusing on the GOMS model, 3) predictive versus descriptive HIP models and examples of each, 4) alternatives to cognitive models like Activity Theory, and 5) conclusions about increasing complexity in models and the need for multidisciplinary approaches.
Behavioural biometrics and cognitive security authentication comparison studyacijjournal
Behavioural
biometrics is a scien
tific study with the primary purpose of identifying the authenticity of a
user based on the way they interact with an authentication mechanism. While Association based password
authentication is a cognitive model of authentication system.
The work done shows the implementation of Keyboard Latency technique for Authentication,
implementation of Association Based Password authentication and comparison among two. There are
several forms of behavioural biometrics such as voice analysis, signat
ure verification, and keystroke
dynamics. In this study, evidence is presented indicating that keystroke dynamics is a viable method not
only for user verification, but also for identification as well. The work presented in this model borrows
ideas from th
e bioinformatics literature such as position specific scoring matrices (motifs) and multiple
sequence alignments to provide a novel approach to user verification and identification within the context
of a keystroke dynamics based user authentication system
. Similarly Cognitive approach can be defined in
many ways of which one is association based Technique for authentication
Biometrics technologies are gaining popularity because they provide more reliable and secure means in the process of authentication and verification of users. Dynamic typing is a kind of behavioral biometrics which uses different methods and techniques to store and analyze the users own way of typing. This paper presents a user authentication methodology using keystroke dynamics through piezo-resistive force sensors. An authentication system has been created checking the total typing time, the typing time between each key typed, the force of key typing and the average typing force. The system checks the user authentication veracity in the act of registration. A common numeric keypad modified with piezo-resistive sensors along with a microcontroller were used as materials. The methodology also uses a statistical classifier for the evaluation of users, a data filter to evaluate samples and a method for determining the individual thresholds of users. The system presented biometric error rates of 7.91% of FRR (false rejection rate), 2.32% of FAR (false acceptance rate) and 4.72% of EER (equal error rate).
This document summarizes a research paper on identifying authorized users based on typing speed comparison. The paper proposes using a user's typing speed and patterns as a behavioral biometric for authentication. It analyzes keystroke dynamics data such as dwell times and flight times between keys. A neural network classifier is used to model users' typing behaviors based on monograph and digraph mappings. The proposed framework achieved reduced false positive and negative rates compared to existing password-based authentication methods. It provides a simple, low-cost way to increase computer security without additional hardware or training for users.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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A Study of Person Identification using Keystroke Dynamics and Statistical Ana...Dr. Amarjeet Singh
In this paper, a basic study of closed-set identification
using keystroke dynamics and simple statistical analysis has
been carried out. Dwell time, flight time and one additional
feature called key affinity are used as user-identifying features.
The timing information is passed through a statistical layer to
produce mean and standard deviation. This information is
combined with key affinity to identify a rank-based person list.
In conclusion, we compare the performance of this setup with
other setups. This work aims to suggest that a keystroke
dynamics system relying on pure statistics as its underlying
algorithm may not be sufficiently accurate.
Design and Implementation of New Encryption algorithm to Enhance Performance...IOSR Journals
This document summarizes a research paper that proposes a new encryption algorithm to improve performance parameters. The algorithm is divided into two phases. Phase 1 involves reversing, swapping, circularly shifting bits of the plaintext and XORing with the key. Phase 2 divides the output into blocks, then recombines the left bits of each block. The paper analyzes avalanche effect and execution time of the proposed algorithm compared to existing algorithms to evaluate its performance. The results show better performance than existing algorithms.
IRJET - Two Model Biometrics Authentication for Locker SystemIRJET Journal
This document discusses two-factor biometric authentication for a locker system using keystroke dynamics and facial recognition. It begins with an abstract describing the issues with single-factor authentication and an introduction to keystroke dynamics biometrics. It then reviews several related works that use keystroke dynamics for authentication purposes. Some approaches use keystroke latencies, while others combine keystroke data with machine learning algorithms. However, keystroke patterns may change over time or situation. Therefore, the proposed system combines keystroke dynamics with facial recognition for more robust user identification. This dual-model approach aims to overcome the limitations of either biometric alone for authentication when accessing a locker system.
SYMMETRIC-KEY BASED PRIVACYPRESERVING SCHEME FOR MINING SUPPORT COUNTSacijjournal
In this paper we study the problem of mining support counts using symmetric-key crypto which is more
efficient than previous work. Consider a scenario that each user has an option (like or unlike) of the
specified product, and a third party wants to obtain the popularity of this product. We design a much more
efficient privacy-preserving scheme for users to prevent the loss of the personal interests. Unlike most
previous works, we do not use any exponential or modular algorithms, but we provide a symmetric-key
based method which can also protect the information. Specifically, our protocol uses a third party that
generates a number of matrixes as each user’s key. Then user uses these key to encrypt their data which is
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Symmetric-Key Based Privacy-Preserving Scheme For Mining Support Countsacijjournal
In this paper we study the problem of mining support counts using symmetric-key crypto which is more
efficient than previous work. Consider a scenario that each user has an option (like or unlike) of the
specified product, and a third party wants to obtain the popularity of this product. We design a much more
efficient privacy-preserving scheme for users to prevent the loss of the personal interests. Unlike most
previous works, we do not use any exponential or modular algorithms, but we provide a symmetric-key
based method which can also protect the information. Specifically, our protocol uses a third party that
generates a number of matrixes as each user’s key. Then user uses these key to encrypt their data which is
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This document discusses enhancing security through token generation in a distributed environment. It proposes a new token generation scheme to encrypt user data with specified key parameters, making resources more robust. The token generation scheme would add security for both authentication and authorization. Existing algorithms focus on encrypting data on the user side, which incurs high computational and communication costs. The document suggests a token generation algorithm for distributed data files that provides secure and dependable server storage while maintaining low overhead. It analyzes related work on token-based authentication and security techniques to provide context.
IRJET - An Enhanced Signature Verification System using KNNIRJET Journal
This document proposes an enhanced signature verification system using K-nearest neighbors (KNN) classification. It discusses how signature verification aims to automatically determine if a biometric sample matches a claimed identity. The proposed system extracts features from signatures and uses KNN to classify signatures as genuine or forgeries. It also reviews related work on signature verification using techniques like artificial immune systems and discusses preprocessing steps like normalization to standardize signature size and reduce variations between signatures.
Different date block size using to evaluate the performance between different...IJCNCJournal
The different computer networks whether wired or wireless are becoming more popular with its high
security aspect. Different security algorithms and technique are using to avoid any aforementioned attacks.
One of these technique is a cryptography technique that makes the data as unreadable during the transfer
hence; there is no chance to reclaim the information. Presently, most of the users are using various media
types and internet to transfer the data but, it has the chance to retrieve the data by using these media types.
The perfect solution for this problem is to provide security on time-to-time basis; this stage is always
significant to the security related community discussions. This paper explains the comparison between the
run time of three different encryption algorithms which are DES, AES and Blowfish The compression
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algorithm followed by AES take less time for running compared to DES.
Efficient authentication for mobile and pervasive computingIGEEKS TECHNOLOGIES
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Guillou-quisquater protocol for user authentication based on zero knowledge p...TELKOMNIKA JOURNAL
Authentication is the act of confirming the validity of someone’s personal data. In the traditional
authentication system, username and password are sent to the server for verification. However, this
scheme is not secure, because the password can be sniffed. In addition, the server will keep the user’s
password for the authentication. This makes the system vulnerable when the database server is hacked.
Zero knowledge authentication allows server to authenticate user without knowing the user’s password. In
this research, this scheme was implemented with Guillou-Quisquater protocol. Two login mechanisms
were used: file-based certificate with key and local storage. Testing phase was carried out based on the
Open Web Application Security Project (OWASP) penetration testing scheme. Furthermore, penetration
testing was also performed by an expert based on Acunetix report. Three potential vulnerabilities were
found and risk estimation was calculated. According to OWASP risk rating, these vulnerabilities were at the
medium level.
Authentication Schemes for Session Passwords using Color and ImagesIJNSA Journal
Textual passwords are the most common method used for authentication. But textual passwords are vulnerable to eves dropping, dictionary attacks, social engineering and shoulder surfing. Graphical passwords are introduced as alternative techniques to textual passwords. Most of the graphical schemes are vulnerable to shoulder surfing. To address this problem, text can be combined with images or colors to generate session passwords for authentication. Session passwords can be used only once and every time a new password is generated. In this paper, two techniques are proposed to generate session passwords using text and colors which are resistant to shoulder surfing. These methods are suitable for Personal Digital Assistants.
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Advanced authentication scheme using a Predefined Keystroke Structure
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 2, April 2014
DOI:10.5121/ijcsit.2014.6212 163
Advanced Authentication Scheme Using a
Predefined Keystroke Structure
Abdulameer K. Hussain and Mohammad M. Alnabhan
Computer Science Department, Jerash University, Jerash, 26150, Jordan
ABSTRACT
This paper presents an advanced keystroke authentication model improving users’ validation strength. The
proposed system is based on defining a keystroke structure for each authorized user, to be used in the user
login attempts. This structure is composed based on two components; the user’s typing time deviation
thresholds; and a unique user secret code which is distributed between password's characters based on
time distances. The strength of the proposed method depends primarily on the amount of information
distributed among typing time, and on reducing the deviation of these times. During the preliminary
evaluation, it was confirmed that the proposed system has achieved an improved authentication level, and
the system model was highly accepted between participating users.
KEYWORDS
Authentication, Keystroke, Dynamics, Predefined structure, Time Distance.
1. INTRODUCTION
Authentication is the process of determining whether a user is allowed to accesses a particular
system or resource. The major objective of authentication system is allowing entities to be
recognized before using resources. Several authentication methods are available starting from
alphanumeric passwords until the use of biometrics and smart cards. However, the use of these
technologies has raised several concerns such as the acceptability and lack of flexibility, and lack
of robustness against imposters. In addition, traditional method such as the couple of username
and passwords are required to be effective for authentication, easy and quickly executable, which
can be considered as conflicting and difficult for humans. However, to guarantee strong
authentication it is required to integrate multiple authentication methods. For example, it is
possible to provide strong authentication in the password authentication scheme by combining it
with keystroke dynamics [1].
Keystroke is a behavioural biometric modality monitoring the way individuals’ type on the
keyboard [2]. The basic idea of keystroke dynamics is based on the assumption that people type
in uniquely different characteristic manners. Hence, this method depends on identifying users
certain habitual typing rhythm patterns [3]. Different names of keystroke dynamics are used:
keyboard dynamics, keystroke analysis, typing biometrics and typing rhythms [8]. One of the
advantages of keystroke dynamics is that it is inexpensive because it can be used without any
additional hardware. In addition, the user acceptance of a keystroke dynamics biometric system is
considered very high [16, 10].
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 2, April 2014
164
Several research works had addressed the usage of keystroke dynamics in improving
authentication systems performance. However, still keystroke drawbacks such as users typing
time deviation needs to be considered. This work considers developing keystroke dynamics to
reach an effective and reliable authentication solution; this was achieved by defining a new
keystroke structure for each authorized user and by solving the deviations in user password typing
time.
2. RELATED WORK
In [12] keystroke dynamics was applied to measure users typing process using statistical
methods, in which users were divided into different groups in order to speed up the required
dynamic computation. Additionally, [4] describes preliminary experimental results describing
using keystroke timing as a basic of authentication system in which a textual material and a
statistical model was developed and used within an experimental study. Most keystroke dynamics
studies had been evaluated using datasets where users typed the same fixed string [7], [6], while
very few of them used different strings for each user [11].
Authors in [9] presented a filtering scheme and adaptation mechanism to improve the stability
and effectiveness of keystroke authentication. In which, the typing characteristics of users are
measured by n-dimensional vectors and an ellipsoidal hypothesis space, which is evolved using a
genetic algorithm. In [13] a novel keystroke dynamics authentication system was presented. This
model utilizes two sets of derived values to constructs a template for identifying the user based on
his typing style. The first set of derived values is computed based on the collected measurements,
and the second set is computed based on the first set of derived values. [14] shows the possibility
of using neural networks especially in static keystroke dynamics verification, in which
researchers created a template for each user by using approximately 30 user samples and 45
impostors samples; where the samples represents the timing information that are extracted from
the typing of the name of the user. In the same concern, the behaviour of user typing was used
along with password based security to achieve enhanced security. This was achieved by analyzing
the basic user behaviours/activities and finally training users by neural network and classifying
them as legal or intruder [15].
In addition, individual’s typing behaviour was considered in [17], in which a new function was
presented to train users through keystroke dynamics and a set of validation rules were applied to
validate system users. Furthermore, keystroke dynamics were utilized in [5] to be associated with
PIN codes used in ATM machines, in which a compromising algorithm was implemented, and
used to withdraw the security threat, that might happen when the imposter get hold of both user-
ID (user card) and password.
Accordingly, several research works has addressed the usage of keystroke dynamics in improving
authentication systems performance. However, still keystroke drawbacks such as users typing
time deviation needs to be considered. This work considers developing keystroke dynamics to
reach an effective and reliable authentication solution; this was achieved by defining a new
keystroke structure for each authorized user and by solving the deviations in user password'
typing time
3. PROPOSED SYSTEM MODEL
This system depends upon constructing a predefined keystroke structure for each user to ensure
improved authentication strength. The system considered a strong users password especially for
sensitive applications. Figure 1 below describes the proposed system steps. The first phase is
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 2, April 2014
165
described as the enrolment phase, in which users are trained several times to enter password
characters before the actual registration phase, in order to measure the typing timing periods and
the deviations thresholds accurately. In addition, the time deviation of typing speed over trails is
also considered enrolment phase.
Table 1 illustrates the procedure of measuring the timing periods between each successive
characters of the password. This table represents a matrix maintaining the time periods between
each successive characters of the password. Suppose, the password consists of n characters, then
T11 to T1m represents the typing time between the first character and the second characters for m
trials, T21 to T2m represents the timing periods between the second characters and the third
characters, and T1n to Tnm represents the typing periods between the character before the last
character of the password and the last character. In addition, two timing thresholds for each
column in table 1 must be identified for each successive character. The upper range threshold
denoted as (th1) and the lower range threshold denoted as (th2), in which the user’s typing time
must lie between these thresholds.
Table 1: Registration Matrix
Trial
No
Typing Time between
character 1 and character 2
Typing Time between
character 2 and character 3
… Typing Time between character n-1
and character n
1 T11 T21 … Tn1
2 T12 T22 Tn2
m T1m T2m Tnm
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 2, April 2014
166
Figure1: Proposed System Model
The second phase is responsible for forming the predefined keystroke structure, which consists of
two parts; the password characters and users unique secret code distributed between these
characters based on the typing time deviation thresholds measured in phase 1. Suppose, the secret
information is S, this can be divided into different parts (S0, S1, … Sn-2) for password of length
n characters, figure 2 below illustrates the predefined structure including both parts:
>=th11<=th12||
S0
>=th21<=th22
|| S1
… >=thn-1<=thn Sn-2
Figure 2: The predefined structure of keystroke Dynamics and Secret Information
Where th11 and th12 represents the upper and lower range of thresholds for the first and second
characters of the password, th21 and th22 represents the upper and lower range of thresholds for
the second and the third password characters. thn-11 and thn represents the upper and lower range
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 2, April 2014
167
of thresholds for the last two password characters before. The last step in the system model, is
described as the login phase, where users entre their passwords. The system calculates the typing
time as in the enrolment phase, and then checks the upper and lower thresholds for each
successive character. If the new entry lies within these thresholds, the user is considered
authenticated and will be successfully logged into the system. If the same authenticated user
makes some distances from the thresholds, then the system rejects that user. In this case, the
system asks the user to retry logging to the system by typing his keystroke structure, which
represents the password characters accompanied with segments of the unique code S. If the
entered structure matches the specific predefined structure being formed for this user, then the
user is considered entirely authenticated.
4. RESULTS AND ANALYSIS
In order to evaluate the proposed system, 10 measurement attempts were conducted allowing
each user to enter password characters, in order to measure typing time deviations (in
millisecond) between two successive characters. Table 2 below summarizes the typing time
deviations for one single user considering 10 trials. The user utilises a strong password consisting
of set of special characters.
Table2: Time distances between successive characters of the use's password (during 10 trials)
1st
char
& 2nd
char.
2nd
&
3nd
char.
3rd
char.
& 4th
char.
4th
char.
& 5th
char.
5th
char.
& 6th
char.
6th
char.
& 7th
char.
7th
&
8th
char.
8th
char.
& 9th
char.
n cha.
& 2nd
char.
Average
time of each
row
H 344 L 218 H 359 187 343 172 L 203 280 156 251
343 H 312 249 H 250 343 203 218 297 141 H 261
280 265 171 188 358 H 219 H 234 H 312 110 237
234 250 187 203 358 188 218 312 H 156 234
250 249 L 156 250 327 156 219 296 141 227
234 234 172 L 187 H 359 218 219 296 L 109 225
281 234 187 203 297 171 219 280 140 223
234 250 156 187 343 187 203 296 109 L 218
234 250 249 219 L 296 187 203 281 125 227
L 234 265 203 265 312 L 141 218 L 234 109 220
Note: H stands for high threshold and L stands for low threshold for each column.
For each user, the time between successive characters are calculated and stored in a specific
profile. The average time distance in each trial is registered measuring the upper range (denoted
as H in Table 2) and the lower range (denoted as L in Table 2); which corresponds to the
thresholds th11 and th22 mentioned in figure 2.
After measuring the time deviation thresholds; the predefined keystroke structure for the
experimental user was formed. The length of the user’s secret code is selected depending on the
user’s password and on the length of each splitting segments of this private information. During
the experimental scenario, the length of the user's password was 9 characters, so the suitable
length of the user secrete code or private information must be at least twice of the password's
length (i.e., 18 characters). For example, the phrase "secureapplications" is selected as the user’s
private information, then the predefined structure after the distribution of this phrase considering
typing time distances as described in table 2, will appear as the following:
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 2, April 2014
168
>=234 <=344se>= 109<=156cu>=218 <=312re>= 156<=359ap>=
187<=250pl>=296 <=359ic >=141 <=219at >=203 <=234io >=218 <=312ns
Figure 3: First sample of the predefined keystroke structure
Using the average of typing time deviations thresholds as described in the last column of table 2;
the shape of the predefined keystroke structure will appear as the following:
>= 218<=261se>= 218<=261cu>= 218<=261re>= 218<=261ap>=
218<=261pl>= 218<=261ic >= 218<=261at >= 218<=261io >=
218<=261ns
Figure 4: Second sample of the predefined structure
The same measurement steps were repeated for 10 participating users, in which the model
acceptance among participants and the authentication successful rate was almost 80% during the
experimental trials. In addition, the presented authentication model has solved the problem of
large deviations in keystroke dynamics. As show in figures 2 and 3, the predefined structures
have shown a strong authentication solution, in which user is considered authenticated after
providing the correct password characters within the right ranges of typing time deviations, or
after entering the password characters accompanied with segments of the unique code
representing his keystroke structure, which should match the predefined structure being formed
for the user during the registration phase as described in figure 1.
5. CONCLUSIONS
The proposed authentication model solves the problem of large deviations in keystroke dynamics
and provides improved keystroke authentication level. This was achieved by defining new
keystroke structure for each system user. The structure consists of two parts; the password
characters and segments of user secrete code distributed among password characters based on
users typing time thresholds. The proposed authentication model consists of several phases;
starting with the enrolment phase which defines the time distance thresholds. Following, the
predefined keystroke structure is formed using the time thresholds and user’s secret code.
Afterwards using the keystroke structure, users can be authenticated and logged in the system. A
set of preliminary measurement trials were conducted evaluating the proposed model phases and
determining the system performance and successful rates. It was confirmed that the system has
achieved a strong authentication level and the system model was highly accepted between
participating users. However, in future measurements it is worth increasing the number of
participating users and using different password and secret codes samples. This will provide an
increased validity to system evaluation process.
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Authors
Mohammad Alnabhan finished his bachelor degree in computer science, from Mu’tah University, in 2004.
He received his master degree in computer science from Anglia Ruskin University (ARU), in 2006.
Alnabhan earned his PhD degree from Brunel University in 2009; his research field was on mobile
computing. Where, he developed an innovative Location Based Services (LBS) model focused towards
disabled pedestrians. After completing his PhD, Mohammad Alnabhan was appointed as an Assistant
Professor in the computer science department at Jerash University, where, he was involved in teaching a
great variety of computer science courses in both undergraduate and postgraduate levels. During his
academic career, alnabhan has confirmed outstanding research ability, where he has published more than
twenty research articles in highly reputed Journals and international conferences. In which, his research
interest includes mobile computing, context adaptive computing, QoS measurements, m-learning, and
Location Based Services (LBS).