Corporate System Users Identification by the Keyboard Handwriting based on Neural Networks
Domanetska Iryna1, Khaddad Anton2, Krasovska Hanna3, Yeremenko Bohdan4
1Domanetska Iryna, Department of Intelligent Technologies, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine.
2Khaddad Anton, department of Cybersecurity and Computer Engineering Kiev National University of Construction and Architecture Kyiv, Ukraine.
3Krasovska Hanna department of Intelligent Technologies, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine.
4Yeremenko Bohdan*, department of information technology design and applied mathematics, Kyiv National University of Construction and Architecture, Kyiv, Ukraine.
Manuscript received on October 14, 2019. | Revised Manuscript received on 23 October, 2019. | Manuscript published on November 10, 2019. | PP: 4156-4161 | Volume-9 Issue-1, November 2019. | Retrieval Number: A6101119119/2019©BEIESP | DOI: 10.35940/ijitee.A6101.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper is devoted to practical information security aspects in the corporate system. Particular attention is paid for the ensuring access control problem for corporate confidential information. Solving this problem involves identifying a person trying to gain unauthorized access to the corporate system and identification of a authorized user committing illegal actions. The paper shows that a person’s keyboard handwriting is determined by behavioural characteristic, which is very difficult to imitate. This means that the person identification based on the keyboard handwriting analysis is the most reliable. It is necessary to consider the possibility of accidental uncontrolled change user’s keyboard handwriting settings when it works. Such changes can be caused by changes in physiological or emotional state. Using neural networks enables to perform analysis of handwriting keyboard considering these changes at the real time. Thus, the artificial neural networks using makes it possible considerably improves the security of corporate confidential information protection from authorized user committing illegal actions. Existing methods and algorithms for user identifying by his keyboard handwriting are focused on using a multilayer perceptron. However, FAM architecture and capabilities analysis of adaptive resonance theory has identified advantages such as the ability to form associative pairs and map the rules of fuzzy inferences to clusters of neural networks in this category. Considering these characteristics was decisive in choosing the neural network intelligent system for users identification by his keyboard handwriting style. As a result of the research proposed generalized architecture Corporate System Users Identification by the Keyboard Handwriting, based on the use of Cascade ARTMAP. The scheme of rules formation in Cascade ARTMAP and the knowledge base formation scheme of Users Identification Intelligent System by Keyboard Handwriting are proposed.
Keywords: Access Control, Information Security, Keyboard Handwriting, Neural Network, Unauthorized Access
Scope of the Article: Artificial Intelligence