Dynamics User Identification Using Pairwise Client Coupling

In this paper due to the increasing vulnerabilities in the internet, security alone isnt sufficient to keep a rupture, however digital crime scene investigation and cyber intelligence is also required to prevent future assaults or to identify the potential attacker. The unpretentious and incognito nature of biometric information gathering of keystroke dynamics has a high potential for use in cyber forensics or cyber intelligence and crime scene investigation or digital knowledge. The keystroke dynamics is a biometric assumption that different people typify in a unique way. The information accessing from computer systems is normally controlled by client accounts with usernames and passwords. If the set of data falls into the wrong hands, such a scheme has little security. For example fingerprints, can be used to strengthen security, however they require very expensive additional hardware. Keystroke dynamics with no additional hardware can be used. Keystroke dynamics is for the most part applicable to verification and identification also possible. In verification it is known who the client is supposed to be and the biometric system should verify if the user is who he claims to be in identification, the biometric The system should identify the client with keystroke dynamics without additional knowledge. This paper examines the usefulness of keystroke dynamics to determine the users identity. We propose three plans for user identification when entering a keyboard. We use different machine learning algorithms in conjunction with the proposed user coupling technology. In particular, we show that combined user coupling in a bottom - up tree structure scheme provides the best performance in terms of both precision and time complexity. The techniques proposed are validated by keystroke data. Lastly, we also examined the performance of the identification system and demonstrated that the performance was not optimal, as expected.