Two-factor user authentication with the CogRAM weightless neural net

Lai, Weng Kin; Tan, Beng Ghee; Soo, Ming Siong; Khan, Imran

doi:10.1109/ijcnn.2014.6889702

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…An advantage of using the CogRAM in such an application is that we can easily implement the neural net in hardware, and some work has been done in this direction by Nitish and his co-investigators [23]. This paper extends some of the earlier work for user identification with the CogRAM weightless neural net which had been used on different passwords [22]. Our future work would be directed into developing a standalone hardware implementation of the CogRAM to process the typing data as they are generated in situ.…”

Section: Discussionmentioning

confidence: 89%

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User Identification of Keystroke Biometric Patterns with the Cognitive RAM Weightless Neural Net

Lai

Tan

Soo

et al. 2016

Lecture Notes in Electrical Engineering

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A user identification system which matches the keystroke dynamics of the users with the Cognitive RAM (CogRAM) weightless neural net is discussed in this paper. The keystroke patterns are made up of a common password for all users. While there are several common approaches to represent the users' keystroke patterns, the approach adopted here is based on the force applied to each key. Effectively, they will then constitute a fixed length passkey. In addition, the system was developed based on an 8-bit AVR enhanced, RISC microcontroller. From the experimental results obtained, it can be seen that the identity of the users can be successfully matched just from their keystroke biometric patterns alone.

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Section: Discussionmentioning

confidence: 89%

“…We have tested this new numerical keypad to authenticate the identity of the users based on their keystroke biometric signatures in our previous work [22]. Each user's keystroke biometric signature was based on their preferred individual password, each of which consists of 8 digits.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

User Identification of Keystroke Biometric Patterns with the Cognitive RAM Weightless Neural Net

Lai

Tan

Soo

et al. 2016

Lecture Notes in Electrical Engineering

Self Cite

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Classification of Keystroke Patterns for User Identification in a Pressure-Based Typing Biometrics System with Particle Swarm Optimization (PSO)

Lai

Tan

Soo

et al. 2015

Neural Information Processing

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A broad review on non-intrusive active user authentication in biometrics

Thomas¹,

Mathew

2021

J Ambient Intell Human Comput

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Authentication is the process of keeping the user's personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.

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Two-factor user authentication with the CogRAM weightless neural net

Cited by 3 publications

References 12 publications

User Identification of Keystroke Biometric Patterns with the Cognitive RAM Weightless Neural Net

User Identification of Keystroke Biometric Patterns with the Cognitive RAM Weightless Neural Net

Classification of Keystroke Patterns for User Identification in a Pressure-Based Typing Biometrics System with Particle Swarm Optimization (PSO)

A broad review on non-intrusive active user authentication in biometrics

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