Mohd Noorulfakhri Yaacob
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Preferred name
Mohd Noorulfakhri Yaacob
Official Name
Mohd Noorulfakhri, Yaacob
Main Affiliation
Scopus Author ID
57218118259

Research Output
Now showing 1 - 6 of 6
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Publication

Soft Biometrics and Its Implementation in Keystroke Dynamics

2020-06-17 , Mohd Noorulfakhri Yaacob , Syed Zulkarnain Syed Idrus , Wan Nor Ashiqin Wan Ali , Wan Azani Wan Mustafa , Mohd Aminudin Jamlos , Wahab M.H.A.

Biometrics is a unique art that exists within a person and allows it to be used to differentiate between one another. These biometrics can be divided into two categories namely behaviour and physical such as face, fingerprint, hand, voice and gait. There are previous studies which examine the personal's characteristic or personality as gender, age, cultural, weight, height, colour of hair etc. This personal's characteristic or personality also known as soft biometric. Several previous studies have shown that the use of soft biometric element (one or combination of elements) in the process of identifying individuals can improve the performance of individual recognition. This paper will elaborate and concludes past studies related to the use of soft biometric elements in KD. Several soft biometric elements applied in various methods of recognition on previous studies have been listed and the results of the studies are compared.

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Publication

A Review on Feature Extraction in Keystroke Dynamics

2020-06-17 , Mohd Noorulfakhri Yaacob , Syed Zulkarnain Syed Idrus , Wan Nor Ashiqin Wan Ali , Wan Azani Wan Mustafa , Mohd Aminudin Jamlos , Wahab M.H.A.

Feature extraction is an important process before an analysis of a data is carry out. Different behaviour of a user while using the keyboard is a feature that need to be identified in the Keystroke Dynamics (KD) study. Example are the difference between typing time between letters, typing speed and the force of a person pressing the keyboard. Past studies related to feature extraction for KD have been described in this paper. Various features that have been used are listed and the results of the study are compared. The results of this writing are expected to help new researchers in the process of evaluating KD.

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Multiple Fusions Approach for Keystroke Dynamics Verification System with Soft Biometrics

2020-09-21 , Mohd Noorulfakhri Yaacob , Syed Zulkarnain Syed Idrus , Wan Azani Wan Mustafa , Mohd Aminudin Jamlos , Helmy Abd Wahab M.

Computer security is a process that controls the entire information system, including network, system and hardware. Important information that must be controlled in a system is the data or information contained in a system. Various methods have been used to ensure that only users with legitimate access to data can use a system. Usernames and passwords have been a common practice by many systems as the first requirement to be fulfilled to access the system, but some systems use the secondary verification for additional confirmation. In this article, Keystroke Dynamics has been used as the user's second level authentication for the systems that use the keyboard to login into a system. A common problem of system intrusions is that the system fails to identify the user who signs in using the keyboard when the login is correct. There is a possibility that someone else tries to break into the system. To ensure and improve users' recognition who use the keyboard to enter their logins into the system, Keystroke Dynamics is used as a next-level verification if the login is correct. Soft biometrics is used in the user authentication process using KD method in this study. The soft biometric elements used in this study are culture, gender, educational level (CGPA - Cumulative Grade Point Average) and region of birth (ROB). All of these four soft biometric elements are expected to enhance capabilities in the user authentication process.

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Enhancing Keystroke Dynamics Accuracy with Optimal SVM Kernel Usage

2024-05-10 , Mohd Noorulfakhri Yaacob , Syed Zulkarnain Syed Idrus , Mohammed A.H.

Behavioral biometric traits are not fully distinguished in recognition tasks, but they can improve the overall performance of biometric recognition systems by adding them. The behavioral biometric studied in this paper is related to keystroke dynamic. This paper examines the touch keystroke dynamics of computer users for the purpose of identifying their culture by using four different SVM kernels. It has been confirmed that racial classifications can be made by gathering keystroke data from 250 respondents representing various culture in Malaysia. Results show that different culture categories display different typing patterns. The classification is made using four SVM kernels and a comparison of the accuracy results is shown. The four kernels are Linear, Quadratic, Cubic and Fine Gaussian. The linear kernel has provided the highest accuracy and consistent readings compared to other kernels for the four features evaluated for dynamic keystrokes, namely press-press time, release-release time, press-release time and release-press time. The linear kernel has the highest accuracy reading of 92.4% for classification using press-press time features for the Malay vs Chinese category.

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Security enhancements for person verification using Multi Model Fusion Keystroke Dynamics and soft Biometrics

2021 , Mohd Noorulfakhri Yaacob

Biometric is used as a main security fence in a computer system. Human’s biometrics can be categorized into three types: morphological, biological and behavioral. This study focuses on behavioral biometric by studying keystroke dynamics. Keystroke dynamics is used as the user’s second level verification for the systems that use the keyboard to login into a system. Soft biometric is a method by which we can identify human identity based on the physical characteristics or behaviors of a person that are naturally produced. A common problem of system intrusions is that the system fails to identify the real user who signs in using the keyboard when the login is correct. A user’s login information can be stolen, guessed, and recorded by special software such as a keylogger. There is a possibility that someone else tries to break into the system by using brute-force and dictionary attacks. To ensure and improve user’s verification who use the keyboard to enter their logins into the system, this study has used multi model fusion methods to combine keystroke dynamics and soft biometric. This study introduces new soft biometric elements in keystroke dynamics that is gender, race, region and educational level. Keystroke dynamics is used to distinguish typing patterns in each of these categories and uses these soft biometric features to further enhance the verification capabilities. Mathematical formulas such as mean, standard deviation and Scale Manhattan Distance have been used to obtain the score of the imposter and genuine user. Multi model fusion methods have been introduced by integrating the score values of several soft biometric elements from classification with scores obtained from imposter and genuine user to improve the verification process. For identification purposes, Support Vector Machine classification method is used to perform this classification for soft biometric identification. The results show that there are significant differences in the typing pattern in the region category. The highest accuracy achieved is 91.17% in this classification process for the region category. Equal error rate (EER) obtained from a multi model fusion approach is 11.33%. which is to incorporate four features of soft biometrics (gender, race, region and educational level) into the verification process. Overall, it is found that the multi fusion process introduced has managed to reduce the EER values for the purpose of verification in keystroke dynamics.

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Weighted-KNN Based Analysis of Typing Patterns Across Different Age Groups

2024-05-10 , Mohd Noorulfakhri Yaacob , Syed Zulkarnain Syed Idrus , Abdul Hapes Mohammed

The use of behavioural biometrics, such as movement patterns and keystroke dynamics, in human identity recognition research to strengthen smartphone security is growing. Users usually secure their phones with a PIN or pattern. This paper uses a smartphone keystroke dynamic open dataset with user age information. This Open Dataset is known as the RHU-Keystroke Dynamics dataset. A dataset classification study was conducted utilising the Weighted K-nearest neighbour (W-KNN) method in order to identify the three age categories with the highest accuracy. The four keystroke features that have been collected in this open dataset are used for this classification. The highest average accuracy obtained from this W-KNN method is 83%. The results of the study are explained with a Confusion Matrix diagram and a Receiver Operating Characteristic (ROC) graph. A classification study using this method has successfully increased accuracy and can be utilized in the use of software, as demonstrated in the results of the study. It is expected that future studies will apply other classification methods to keystroke dynamics.

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