ABSTRACT
Cyber crime also known as computer oriented crimes is refers to misuse of computer and network equipment to steal, modify and damage the data for particular purpose. It’s been used to threaten people security personally. Computer crimes are difficult to be detected and proven due to the cyber crime happens virtually and cannot be proven physically by the easy way. Nowadays cyber crimes mostly remain unsolved due to limitation of evidence gathering process from security experts to identify the potential attacker. There are method that can be used to searching criminals by narrow down the possibility of the criminal identity by identifying their gender. The gender identifying is useful to department of cyber security to take further action for criminal that involve with cyber crime. Keystroke dynamics is an alternative approach to identifying the gender of criminal. Keystroke dynamics is known as behavioural biometric that refers to the rhythm of the individual typing on a touch keyboard based on the manner which is a automated method of identity identifying. Keystroke dynamics capture the individual unique behavioural characteristic of typing rhythm and it will automated generate dataset type of gender by recording type of pattern from a group of mobile user. The gender are classified on the criteria that meet the requirement of the touchscreen keyboard features types. In this project the artificial neural network algorithm will applied. Artificial neural network will create the signature to pattern type from individual and differentiate their data classification type whether male or female. It is anticipated that this will bring a great contribution to the investigators by providing information of gender for the investigations.
PROJECT BACKGROUND
Biometrics is consist of keystroke dynamics, mouse dynamics, fingerprints, voice, face that known as nonintrusive that unrequire capture information biometrics using specialized hardware. Biometrics is classified into two parts which is physiological and behavioural biometrics. Keystroke dynamics and signature verification are some examples of behavioural biometrics.
Keystroke dynamics is a behavioural biometrics that aims to identify users based on the typing of the individuals such as duration of a keystroke, key hold time, latency of keystroke, force of keystrokes and others from numerous of input devices from keyboard to touchscreen based keyboards. Many previous studies have demonstrated that keystroke dynamics has potential and ability as a biometrics approach for identifying the gender that do not require high cost.
Gender is a type of soft biometric that will help the cyber intelligence to investigate and get a relevant information of the person that involve with cyber criminal. Gender classification has been successfully applied in several biometric identification based on face, speech, iris or gait recognition.
Neural Network is defined as a network composed of a number interconnected units. It is designed in a way in order to seek computing of human brain style. As a result, it is powerful enough to variety of problem been solve that are proved to be difficult with conventional digital computational methods. Neural network can detect all complex nonlinear relationship between input and outputs which does not require excessive statistical training .
In this project, 51 student keystroke features data based on mobile phone will be extracted that include flight times and dwell times based on previous research paper which is consist of 51 student male and female are required to typed a unique password to extracted their dynamic keystroke feature. Other than that, The weka Tool are been used to train the existed data and test their accuracy on classification of gender.
Keystroke dynamics is a behavioural biometrics that aims to identify users based on the typing of the individuals such as duration of a keystroke, key hold time, latency of keystroke, force of keystrokes and others from numerous of input devices from keyboard to touchscreen based keyboards. Many previous studies have demonstrated that keystroke dynamics has potential and ability as a biometrics approach for identifying the gender that do not require high cost.
Gender is a type of soft biometric that will help the cyber intelligence to investigate and get a relevant information of the person that involve with cyber criminal. Gender classification has been successfully applied in several biometric identification based on face, speech, iris or gait recognition.
Neural Network is defined as a network composed of a number interconnected units. It is designed in a way in order to seek computing of human brain style. As a result, it is powerful enough to variety of problem been solve that are proved to be difficult with conventional digital computational methods. Neural network can detect all complex nonlinear relationship between input and outputs which does not require excessive statistical training .
In this project, 51 student keystroke features data based on mobile phone will be extracted that include flight times and dwell times based on previous research paper which is consist of 51 student male and female are required to typed a unique password to extracted their dynamic keystroke feature. Other than that, The weka Tool are been used to train the existed data and test their accuracy on classification of gender.
PROBLEM STATEMENT
Gender identifying is one step to solve the cyber crime. The most common approach for detecting the cyber criminal identity based on their gender in the investigation is using several types of biometrics data such as face, iris, speech recognition and others. This method is greatly though to implement for cyber intelligent as it cannot capture the information of cyber criminal intrusion occur and the cost of implementation is high rather than keystroke dynamics biometric. Therefore, nowadays cyber crimes remain unsolved due to limitation of evidence gathering process to identify the potential attacker .
OBJECTIVES
FRAMEWORK
ALGORITHM & TECHNIQUES USED
Using Artificial Neural Network to train and test data
RESULTS
CONCLUSION
Effectiveness on classified gender based on model data is depend of the number sample of system. The biometrics approach gives many benefits to cyber security world to do the task in finding criminal of virtual world.This Biometrics method should produce more holistic and effective cost for finding the cyber criminal.
STUDENT PROFILE
SITI HAJAR MAT ZAN
Student ID : BTBL15040514
Supervisor : Dr. Mohamad Afeende Bin Mohamed
Course : Bachelor of Computer Science Network Security (Hons)
Institution : Universiti Sultan Zainal Abidin Terengganu (UniSZA)
Contact Number : +60188708433
Email : [email protected]